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NiuTrans
Toy-MT-Introduction
Commits
bd87e7ad
Commit
bd87e7ad
authored
Nov 24, 2021
by
zengxin
Browse files
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合并分支 'master' 到 'zengxin'
Master 查看合并请求
!295
parents
e10fac8e
dee6cf16
隐藏空白字符变更
内嵌
并排
正在显示
17 个修改的文件
包含
1071 行增加
和
1074 行删除
+1071
-1074
Book/Chapter3/Figures/figure-human-translation.tex
+2
-2
Book/Chapter3/Figures/figure-noise-channel-model.tex
+2
-1
Book/Chapter3/Figures/figure-process-of-machine-translation.tex
+2
-2
Book/Chapter3/Figures/greedy-mt-decoding-process-1.tex
+6
-6
Book/Chapter3/Figures/greedy-mt-decoding-process-3.tex
+6
-6
Book/Chapter3/chapter3.tex
+12
-12
Book/Chapter4/Figures/grid-search-2.tex
+0
-43
Book/Chapter4/Figures/grid-search.tex
+44
-2
Book/Chapter4/Figures/search-space-representation-of-feature-weight-1.tex
+0
-30
Book/Chapter4/Figures/search-space-representation-of-feature-weight-2.tex
+0
-41
Book/Chapter4/Figures/search-space-representation-of-feature-weight.tex
+69
-2
Book/Chapter4/chapter4.tex
+2
-6
Book/ChapterAppend/chapterappend.tex
+20
-20
Book/mt-book-xelatex.idx
+526
-526
Book/mt-book-xelatex.ptc
+359
-354
Section03-Word-Based-Models/section03.tex
+19
-19
Section05-Neural-Networks-and-Language-Modeling/section05.tex
+2
-2
没有找到文件。
Book/Chapter3/Figures/figure-human-translation.tex
查看文件 @
bd87e7ad
...
@@ -8,7 +8,7 @@
...
@@ -8,7 +8,7 @@
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感到
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...
@@ -38,7 +38,7 @@
...
@@ -38,7 +38,7 @@
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...
...
Book/Chapter3/Figures/figure-noise-channel-model.tex
查看文件 @
bd87e7ad
...
@@ -9,7 +9,8 @@
...
@@ -9,7 +9,8 @@
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信源
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...
...
Book/Chapter3/Figures/figure-process-of-machine-translation.tex
查看文件 @
bd87e7ad
...
@@ -5,7 +5,7 @@
...
@@ -5,7 +5,7 @@
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[anchor=west] (s1) at (0,0)
{{
我
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[anchor=west] (s2) at ([xshift=2em]s1.east)
{{
对
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[anchor=west] (s2) at ([xshift=2em]s1.east)
{{
对
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[anchor=west] (s3) at ([xshift=2em]s2.east)
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你
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[anchor=west] (s4) at ([xshift=2em]s3.east)
{{
表示
}}
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[anchor=west] (s4) at ([xshift=2em]s3.east)
{{
感到
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[anchor=west] (s5) at ([xshift=2em]s4.east)
{{
满意
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[anchor=west] (s5) at ([xshift=2em]s4.east)
{{
满意
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\node
[anchor=south west] (sentlabel) at ([yshift=-0.5em]s1.north west)
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待翻译句子(已经分词):
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{
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待翻译句子(已经分词):
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...
@@ -35,7 +35,7 @@
...
@@ -35,7 +35,7 @@
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[anchor=north west,inner sep=1pt,fill=black] (tl31) at (t31.north west)
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[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3em] (t41) at ([yshift=-1em]s4.south)
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feel
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...
...
Book/Chapter3/Figures/greedy-mt-decoding-process-1.tex
查看文件 @
bd87e7ad
...
@@ -16,7 +16,7 @@
...
@@ -16,7 +16,7 @@
\node
[anchor=west] (s1) at (0,0)
{{
我
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对
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对
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你
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...
@@ -53,8 +53,8 @@
...
@@ -53,8 +53,8 @@
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...
@@ -121,7 +121,7 @@
...
@@ -121,7 +121,7 @@
\node
[anchor=west] (s1) at (0,0)
{{
我
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{{
我
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[anchor=west] (s2) at ([xshift=3em]s1.east)
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对
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...
@@ -160,8 +160,8 @@
...
@@ -160,8 +160,8 @@
{
{
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...
...
Book/Chapter3/Figures/greedy-mt-decoding-process-3.tex
查看文件 @
bd87e7ad
...
@@ -11,7 +11,7 @@
...
@@ -11,7 +11,7 @@
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...
@@ -50,8 +50,8 @@
...
@@ -50,8 +50,8 @@
{
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...
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...
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;
...
@@ -215,8 +215,8 @@
...
@@ -215,8 +215,8 @@
{
{
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t41) at ([yshift=-1.3em]s4.south)
{$
\phi
$}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t41) at ([yshift=-1.3em]s4.south)
{$
\phi
$}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t42) at ([yshift=-0.2em]t41.south)
{
show
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t42) at ([yshift=-0.2em]t41.south)
{
feel
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t43) at ([yshift=-0.2em]t42.south)
{
show
s
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t43) at ([yshift=-0.2em]t42.south)
{
feel
s
}
;
}
}
...
...
Book/Chapter3/chapter3.tex
查看文件 @
bd87e7ad
...
@@ -111,7 +111,7 @@
...
@@ -111,7 +111,7 @@
%----------------------------------------------
%----------------------------------------------
\vspace
{
-0.2em
}
\vspace
{
-0.2em
}
\parinterval
图
\ref
{
fig:3-3
}
展示了人在翻译``我
对 你表示
满意''时可能会思考的内容。具体来说，有如下两方面内容。
\parinterval
图
\ref
{
fig:3-3
}
展示了人在翻译``我
\;
对
\;
你
\;
感到
\;
满意''时可能会思考的内容。具体来说，有如下两方面内容。
\begin{itemize}
\begin{itemize}
\vspace
{
0.5em
}
\vspace
{
0.5em
}
...
@@ -243,9 +243,9 @@
...
@@ -243,9 +243,9 @@
\begin{example}
\begin{example}
一个汉英互译的句对
一个汉英互译的句对
\qquad\qquad\quad
$
\mathbf
{
s
}$
= 机器
\quad
{
\color
{
red
}
翻译
}
\;
就
\;
是
\;
用
\;
计算机
\;
来
\;
进行
\;
{
\color
{
red
}
翻译
}
$
\mathbf
{
s
}$
= 机器
\quad
{
\color
{
red
}
翻译
}
\;
就
\;
是
\;
用
\;
计算机
\;
来
\;
生成
\;
{
\color
{
red
}
翻译
}
\;
的
\;
过程
\qquad\qquad\quad
$
\mathbf
{
t
}$
= machine
\;
{
\color
{
red
}
translation
}
\;
is
\;
just
\;
{
\color
{
red
}
translation
}
\;
by
\;
computer
$
\mathbf
{
t
}$
= machine
\;
{
\color
{
red
}
translation
}
\;
is
\;
a
\;
process
\;
of
\;
generating
\;
a
\;
{
\color
{
red
}
translation
}
\;
by
\;
computer
\label
{
eg:3-1
}
\label
{
eg:3-1
}
\end{example}
\end{example}
...
@@ -253,14 +253,14 @@
...
@@ -253,14 +253,14 @@
\begin{eqnarray}
\begin{eqnarray}
\textrm
{
P
}
(
\text
{
``翻译''
}
,
\text
{
``translation''
}
;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\frac
{
c(
\textrm
{
``翻译''
}
,
\textrm
{
``translation''
}
;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
}{
\sum
_{
x',y'
}
c(x',y';
\mathbf
{
s
}
,
\mathbf
{
t
}
)
}
\nonumber
\\
\textrm
{
P
}
(
\text
{
``翻译''
}
,
\text
{
``translation''
}
;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\frac
{
c(
\textrm
{
``翻译''
}
,
\textrm
{
``translation''
}
;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
}{
\sum
_{
x',y'
}
c(x',y';
\mathbf
{
s
}
,
\mathbf
{
t
}
)
}
\nonumber
\\
&
=
&
\frac
{
4
}{
|
\mathbf
{
s
}
|
\times
|
\mathbf
{
t
}
|
}
\nonumber
\\
&
=
&
\frac
{
4
}{
|
\mathbf
{
s
}
|
\times
|
\mathbf
{
t
}
|
}
\nonumber
\\
&
=
&
\frac
{
4
}{
63
}
&
=
&
\frac
{
4
}{
121
}
\label
{
eq:3-2
}
\label
{
eq:3-2
}
\end{eqnarray}
\end{eqnarray}
\noindent
这里运算
$
|
\cdot
|
$
表示句子长度。类似的，可以得到``机器''和``translation''、``机器''和``look''的单词翻译概率：
\noindent
这里运算
$
|
\cdot
|
$
表示句子长度。类似的，可以得到``机器''和``translation''、``机器''和``look''的单词翻译概率：
\begin{eqnarray}
\begin{eqnarray}
\textrm
{
P
}
(
\text
{
``机器''
}
,
\text
{
``translation''
}
;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\frac
{
2
}{
63
}
\\
\textrm
{
P
}
(
\text
{
``机器''
}
,
\text
{
``translation''
}
;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\frac
{
2
}{
121
}
\\
\textrm
{
P
}
(
\text
{
``机器''
}
,
\text
{
``look''
}
;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\frac
{
0
}{
63
}
\textrm
{
P
}
(
\text
{
``机器''
}
,
\text
{
``look''
}
;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\frac
{
0
}{
121
}
\label
{
eq:3-3
}
\label
{
eq:3-3
}
\end{eqnarray}
\end{eqnarray}
...
@@ -283,13 +283,13 @@
...
@@ -283,13 +283,13 @@
\begin{example}
\begin{example}
两个汉英互译的句对
两个汉英互译的句对
\qquad\qquad
\;
$
\mathbf
{
s
}^
1
$
= 机器
\quad
{
\color
{
red
}
翻译
}
\;
就
\;
是
\;
用
\;
计算机
\;
来
\;
进行
\;
{
\color
{
red
}
翻译
}
$
\mathbf
{
s
}^{
[
1
]
}$
= 机器
\quad
{
\color
{
red
}
翻译
}
\;
就
\;
是
\;
用
\;
计算机
\;
来
\;
生成
\;
{
\color
{
red
}
翻译
}
\;
的
\;
过程
\qquad\qquad\;
$
\mathbf
{
s
}^
1
$
= Machine
\;
{
\color
{
red
}
translation
}
\;
is
\;
just
\;
{
\color
{
red
}
translation
}
\;
by
\;
computer
$
\mathbf
{
t
}^{
[
1
]
}$
= machine
\;
{
\color
{
red
}
translation
}
\;
is
\;
a
\;
process
\;
of
\;
generating
\;
a
\;
{
\color
{
red
}
translation
}
\;
by
\;
computer
\qquad\qquad\;
$
\mathbf
{
s
}^
2
$
= 那
\quad
人工
\quad
{
\color
{
red
}
翻译
}
\quad
呢
\quad
?
$
\mathbf
{
s
}^{
[
2
]
}
$
= 那
\quad
人工
\quad
{
\color
{
red
}
翻译
}
\quad
呢
\quad
?
\qquad\qquad\;
$
\mathbf
{
t
}^
2
$
= So
\;
,
\;
what
\;
is
\;
human
\;
{
\color
{
red
}
translation
}
\;
?
$
\mathbf
{
t
}^{
[
2
]
}
$
= So
\;
,
\;
what
\;
is
\;
human
\;
{
\color
{
red
}
translation
}
\;
?
\label
{
eg:3-2
}
\label
{
eg:3-2
}
\end{example}
\end{example}
...
@@ -298,8 +298,8 @@
...
@@ -298,8 +298,8 @@
\begin{eqnarray}
\begin{eqnarray}
{
\textrm
{
P
}
(
\textrm
{
``翻译''
}
,
\textrm
{
``translation''
}
)
}
&
=
&
{
\frac
{
c(
\textrm
{
``翻译''
}
,
\textrm
{
``translation''
}
;
\mathbf
{
s
}^{
[1]
}
,
\mathbf
{
t
}^{
[1]
}
)+c(
\textrm
{
``翻译''
}
,
\textrm
{
``translation''
}
;
\mathbf
{
s
}^{
[2]
}
,
\mathbf
{
t
}^{
[2]
}
)
}{
\sum
_{
x',y'
}
c(x',y';
\mathbf
{
s
}^{
[1]
}
,
\mathbf
{
t
}^{
[1]
}
) +
\sum
_{
x',y'
}
c(x',y';
\mathbf
{
s
}^{
[2]
}
,
\mathbf
{
t
}^{
[2]
}
)
}}
\nonumber
\\
{
\textrm
{
P
}
(
\textrm
{
``翻译''
}
,
\textrm
{
``translation''
}
)
}
&
=
&
{
\frac
{
c(
\textrm
{
``翻译''
}
,
\textrm
{
``translation''
}
;
\mathbf
{
s
}^{
[1]
}
,
\mathbf
{
t
}^{
[1]
}
)+c(
\textrm
{
``翻译''
}
,
\textrm
{
``translation''
}
;
\mathbf
{
s
}^{
[2]
}
,
\mathbf
{
t
}^{
[2]
}
)
}{
\sum
_{
x',y'
}
c(x',y';
\mathbf
{
s
}^{
[1]
}
,
\mathbf
{
t
}^{
[1]
}
) +
\sum
_{
x',y'
}
c(x',y';
\mathbf
{
s
}^{
[2]
}
,
\mathbf
{
t
}^{
[2]
}
)
}}
\nonumber
\\
&
=
&
\frac
{
4 + 1
}{
|
\mathbf
{
s
}^{
[1]
}
|
\times
|
\mathbf
{
t
}^{
[1]
}
| + |
\mathbf
{
s
}^{
[2]
}
|
\times
|
\mathbf
{
t
}^{
[2]
}
|
}
\nonumber
\\
&
=
&
\frac
{
4 + 1
}{
|
\mathbf
{
s
}^{
[1]
}
|
\times
|
\mathbf
{
t
}^{
[1]
}
| + |
\mathbf
{
s
}^{
[2]
}
|
\times
|
\mathbf
{
t
}^{
[2]
}
|
}
\nonumber
\\
&
=
&
\frac
{
4 + 1
}{
9
\times
7
+ 5
\times
7
}
\nonumber
\\
&
=
&
\frac
{
4 + 1
}{
11
\times
11
+ 5
\times
7
}
\nonumber
\\
&
=
&
\frac
{
5
}{
98
}
&
=
&
\frac
{
5
}{
156
}
\label
{
eq:3-5
}
\label
{
eq:3-5
}
\end{eqnarray}
\end{eqnarray}
}
}
...
...
Book/Chapter4/Figures/grid-search-2.tex
deleted
100644 → 0
查看文件 @
e10fac8e
\begin{tikzpicture}
\begin{scope}
[scale=0.62]
{
\tiny
\draw
[step=1,help lines,color=black]
(0,0) grid (4,4);
\node
[anchor=north]
(y2) at ([xshift=-3.3em,yshift=0em]n1.north)
{
0.01
}
;
\node
[anchor=north]
(y1) at ([xshift=0em,yshift=-3.3em]y2.south)
{
0.00
}
;
\node
[anchor=north]
(y3) at ([xshift=0em,yshift=4.5em]y2.north)
{
0.02
}
;
\node
[anchor=north]
(y4) at ([xshift=0em,yshift=6.6em]y3.north)
{$
\vdots
$}
;
\node
[anchor=north]
(y5) at ([xshift=0em,yshift=2em]y4.north)
{
1.00
}
;
\node
[anchor=north]
(x1) at ([xshift=2em,yshift=-3em]n1.south)
{$
\lambda
_
1
$}
;
\node
[anchor=north]
(x2) at ([xshift=4.5em,yshift=0em]x1.north)
{$
\lambda
_
2
$}
;
\node
[anchor=north]
(x3) at ([xshift=4em,yshift=-1em]x2.north)
{$
...
$}
;
\node
[anchor=north]
(x4) at ([xshift=5em,yshift=1em]x3.north)
{$
\lambda
_{
M
-
1
}$}
;
\node
[anchor=north]
(x5) at ([xshift=5em,yshift=0em]x4.north)
{$
\lambda
_
M
$}
;
\draw
[-](n1) (0,4) -- (0,4.4);
\draw
[-](n2) (1,4) -- (1,4.4);
\draw
[-](n3) (2,4) -- (2,4.4);
\draw
[-](n4) (3,4) -- (3,4.4);
\draw
[-](n5) (4,4) -- (4,4.4);
\node
[anchor=center,draw,circle,inner sep=1.5pt,red!30,fill=red!30] (r31) at (2,4)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,red!30,fill=red!30] (r32) at (2,0)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,red!30,fill=red!30] (r33) at (2,2)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,red!30,fill=red!30] (r35) at (2,1)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,ugreen!50,fill=ugreen!50] (r34) at (2,3)
{}
;
\draw
[-,very thick,red!50, dashed] (1,2) -- (2,4) -- (3,2) -- (2,3) -- (1,2) -- (3,2) -- (2,1) -- (1,2) -- (2,0) -- (3,2);
\draw
[-,very thick,blue!50] (0,1) -- (1,2);
\draw
[-,very thick,blue!50] (3,2) -- (4,4);
\draw
[-,very thick,ugreen!50, dashed] (1,2) -- (2,3) -- (3,2);
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r11) at (0,1)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r12) at (1,2)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r14) at (3,2)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r15) at (4,4)
{}
;
}
\end{scope}
\end{tikzpicture}
\ No newline at end of file
Book/Chapter4/Figures/grid-search
-1
.tex
→
Book/Chapter4/Figures/grid-search.tex
查看文件 @
bd87e7ad
...
@@ -3,13 +3,13 @@
...
@@ -3,13 +3,13 @@
{
\tiny
{
\tiny
\draw
[step=1,help lines,color=black]
(0,0) grid (4,4);
\draw
[step=1,help lines,color=black]
(0,0) grid (4,4);
\node
[anchor=north]
(y2) at (
[xshift=-3.3em,yshift=0em]n1.north
)
{
0.01
}
;
\node
[anchor=north]
(y2) at (
-5.3em,1.5
)
{
0.01
}
;
\node
[anchor=north]
(y1) at ([xshift=0em,yshift=-3.3em]y2.south)
{
0.00
}
;
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[anchor=north]
(y1) at ([xshift=0em,yshift=-3.3em]y2.south)
{
0.00
}
;
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[anchor=north]
(y3) at ([xshift=0em,yshift=4.5em]y2.north)
{
0.02
}
;
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(y3) at ([xshift=0em,yshift=4.5em]y2.north)
{
0.02
}
;
\node
[anchor=north]
(y4) at ([xshift=0em,yshift=6.6em]y3.north)
{$
\vdots
$}
;
\node
[anchor=north]
(y4) at ([xshift=0em,yshift=6.6em]y3.north)
{$
\vdots
$}
;
\node
[anchor=north]
(y5) at ([xshift=0em,yshift=2em]y4.north)
{
1.00
}
;
\node
[anchor=north]
(y5) at ([xshift=0em,yshift=2em]y4.north)
{
1.00
}
;
\node
[anchor=north]
(x1) at (
[xshift=2em,yshift=-3em]n1.south
)
{$
\lambda
_
1
$}
;
\node
[anchor=north]
(x1) at (
1em,-3em
)
{$
\lambda
_
1
$}
;
\node
[anchor=north]
(x2) at ([xshift=4.5em,yshift=0em]x1.north)
{$
\lambda
_
2
$}
;
\node
[anchor=north]
(x2) at ([xshift=4.5em,yshift=0em]x1.north)
{$
\lambda
_
2
$}
;
\node
[anchor=north]
(x3) at ([xshift=4em,yshift=-1em]x2.north)
{$
...
$}
;
\node
[anchor=north]
(x3) at ([xshift=4em,yshift=-1em]x2.north)
{$
...
$}
;
\node
[anchor=north]
(x4) at ([xshift=5em,yshift=1em]x3.north)
{$
\lambda
_{
M
-
1
}$}
;
\node
[anchor=north]
(x4) at ([xshift=5em,yshift=1em]x3.north)
{$
\lambda
_{
M
-
1
}$}
;
...
@@ -44,4 +44,45 @@
...
@@ -44,4 +44,45 @@
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r15) at (4,4)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r15) at (4,4)
{}
;
}
}
\end{scope}
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\begin{scope}
[scale=0.62,xshift=3in]
{
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(y2) at (-5.3em,1.5)
{
0.01
}
;
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{
0.00
}
;
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[anchor=north]
(y3) at ([xshift=0em,yshift=4.5em]y2.north)
{
0.02
}
;
\node
[anchor=north]
(y4) at ([xshift=0em,yshift=6.6em]y3.north)
{$
\vdots
$}
;
\node
[anchor=north]
(y5) at ([xshift=0em,yshift=2em]y4.north)
{
1.00
}
;
\node
[anchor=north]
(x1) at (1em,-3em)
{$
\lambda
_
1
$}
;
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[anchor=north]
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{$
\lambda
_
2
$}
;
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[anchor=north]
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{$
...
$}
;
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[anchor=north]
(x4) at ([xshift=5em,yshift=1em]x3.north)
{$
\lambda
_{
M
-
1
}$}
;
\node
[anchor=north]
(x5) at ([xshift=5em,yshift=0em]x4.north)
{$
\lambda
_
M
$}
;
\draw
[-](n1) (0,4) -- (0,4.4);
\draw
[-](n2) (1,4) -- (1,4.4);
\draw
[-](n3) (2,4) -- (2,4.4);
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[-](n4) (3,4) -- (3,4.4);
\draw
[-](n5) (4,4) -- (4,4.4);
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[anchor=center,draw,circle,inner sep=1.5pt,red!30,fill=red!30] (r31) at (2,4)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,red!30,fill=red!30] (r32) at (2,0)
{}
;
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[anchor=center,draw,circle,inner sep=1.5pt,red!30,fill=red!30] (r33) at (2,2)
{}
;
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[anchor=center,draw,circle,inner sep=1.5pt,red!30,fill=red!30] (r35) at (2,1)
{}
;
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[anchor=center,draw,circle,inner sep=1.5pt,ugreen!50,fill=ugreen!50] (r34) at (2,3)
{}
;
\draw
[-,very thick,red!50, dashed] (1,2) -- (2,4) -- (3,2) -- (2,3) -- (1,2) -- (3,2) -- (2,1) -- (1,2) -- (2,0) -- (3,2);
\draw
[-,very thick,blue!50] (0,1) -- (1,2);
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[-,very thick,blue!50] (3,2) -- (4,4);
\draw
[-,very thick,ugreen!50, dashed] (1,2) -- (2,3) -- (3,2);
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[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r11) at (0,1)
{}
;
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{}
;
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[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r14) at (3,2)
{}
;
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[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r15) at (4,4)
{}
;
}
\end{scope}
\end{tikzpicture}
\end{tikzpicture}
\ No newline at end of file
Book/Chapter4/Figures/search-space-representation-of-feature-weight-1.tex
deleted
100644 → 0
查看文件 @
e10fac8e
\begin{tikzpicture}
\begin{scope}
[scale=0.55]
{
\tiny
\draw
[step=1,help lines,color=black]
grid (4,4);
\node
[anchor=north]
(y2) at ([xshift=-3.3em,yshift=0em]n1.north)
{
0.01
}
;
\node
[anchor=north]
(y1) at ([xshift=0em,yshift=-3.3em]y2.south)
{
0.00
}
;
\node
[anchor=north]
(y3) at ([xshift=0em,yshift=4.5em]y2.north)
{
0.02
}
;
\node
[anchor=north]
(y4) at ([xshift=0em,yshift=6.6em]y3.north)
{$
\vdots
$}
;
\node
[anchor=north]
(y5) at ([xshift=0em,yshift=2em]y4.north)
{
1.00
}
;
\node
[anchor=north]
(x1) at ([xshift=2em,yshift=-3em]n1.south)
{$
\lambda
_
1
$}
;
\node
[anchor=north]
(x2) at ([xshift=4.5em,yshift=0em]x1.north)
{$
\lambda
_
2
$}
;
\node
[anchor=north]
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{$
...
$}
;
\node
[anchor=north]
(x4) at ([xshift=5em,yshift=1em]x3.north)
{$
\lambda
_{
M
-
1
}$}
;
\node
[anchor=north]
(x5) at ([xshift=5em,yshift=0em]x4.north)
{$
\lambda
_
M
$}
;
\draw
[-](n1) (0,4) -- (0,4.4);
\draw
[-](n2) (1,4) -- (1,4.4);
\draw
[-](n3) (2,4) -- (2,4.4);
\draw
[-](n4) (3,4) -- (3,4.4);
\draw
[-](n5) (4,4) -- (4,4.4);
\draw
[decorate,decoration={brace}]
(0,4.7) --(4,4.7) node [xshift=-4em,yshift=1.5em,align=center](label1)
{
M dimensions
}
;
\draw
[decorate,decoration={brace}]
(4.5,4.3) --(4.5,0) node [xshift=2.3em,yshift=5.8em,align=center](label2)
{
Values
}
;
}
\end{scope}
\end{tikzpicture}
\ No newline at end of file
Book/Chapter4/Figures/search-space-representation-of-feature-weight-2.tex
deleted
100644 → 0
查看文件 @
e10fac8e
\begin{tikzpicture}
\begin{scope}
[scale=0.55]
{
\tiny
\draw
[step=1,help lines,color=black]
grid (4,4);
\node
[anchor=north]
(y2) at ([xshift=-3.3em,yshift=0em]n1.north)
{
0.01
}
;
\node
[anchor=north]
(y1) at ([xshift=0em,yshift=-3.3em]y2.south)
{
0.00
}
;
\node
[anchor=north]
(y3) at ([xshift=0em,yshift=4.5em]y2.north)
{
0.02
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;
\node
[anchor=north]
(y4) at ([xshift=0em,yshift=6.6em]y3.north)
{$
\vdots
$}
;
\node
[anchor=north]
(y5) at ([xshift=0em,yshift=2em]y4.north)
{
1.00
}
;
\node
[anchor=north]
(x1) at ([xshift=2em,yshift=-3em]n1.south)
{$
\lambda
_
1
$}
;
\node
[anchor=north]
(x2) at ([xshift=4.5em,yshift=0em]x1.north)
{$
\lambda
_
2
$}
;
\node
[anchor=north]
(x3) at ([xshift=4em,yshift=-1em]x2.north)
{$
...
$}
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[anchor=north]
(x4) at ([xshift=5em,yshift=1em]x3.north)
{$
\lambda
_{
M
-
1
}$}
;
\node
[anchor=north]
(x5) at ([xshift=5em,yshift=0em]x4.north)
{$
\lambda
_
M
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;
\draw
[-](n1) (0,4) -- (0,4.4);
\draw
[-](n2) (1,4) -- (1,4.4);
\draw
[-](n3) (2,4) -- (2,4.4);
\draw
[-](n4) (3,4) -- (3,4.4);
\draw
[-](n5) (4,4) -- (4,4.4);
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r11) at (0,1)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r12) at (1,2)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r13) at (2,1)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r14) at (3,2)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r15) at (4,4)
{}
;
\draw
[-,very thick,blue!50] (0,1) -- (1,2) -- (2,1) -- (3,2) -- (4,4);
\node
[anchor=north]
(p1) at ([xshift=5em,yshift=13em]n5.north)
{
\scriptsize
{$
\leftarrow
$
\textbf
{
path
}
:
}}
;
\node
[anchor=north]
(e1) at ([xshift=0,yshift=-0.4em]p1.south)
{$
w
_
1
=
0
.
01
$}
;
\node
[anchor=north]
(e2) at ([xshift=0,yshift=-0.8em]e1.south)
{$
w
_
2
=
0
.
02
$}
;
\node
[anchor=north]
(e3) at ([xshift=0,yshift=0.4em]e2.south)
{$
\vdots
$}
;
\node
[anchor=north]
(e4) at ([xshift=0,yshift=-0.2em]e3.south)
{$
w
_
M
=
1
.
00
$}
;
}
\end{scope}
\end{tikzpicture}
\ No newline at end of file
Book/Chapter4/Figures/search-space-representation-of-feature-weight
-3
.tex
→
Book/Chapter4/Figures/search-space-representation-of-feature-weight.tex
查看文件 @
bd87e7ad
...
@@ -3,13 +3,80 @@
...
@@ -3,13 +3,80 @@
{
\tiny
{
\tiny
\draw
[step=1,help lines,color=black]
grid (4,4);
\draw
[step=1,help lines,color=black]
grid (4,4);
\node
[anchor=north]
(y2) at ([xshift=-3.3em,yshift=0em]n1.north)
{
0.01
}
;
\draw
[-](n1) (0,4) -- (0,4.4);
\draw
[-](n2) (1,4) -- (1,4.4);
\draw
[-](n3) (2,4) -- (2,4.4);
\draw
[-](n4) (3,4) -- (3,4.4);
\draw
[-](n5) (4,4) -- (4,4.4);
\node
[anchor=north]
(y2) at (-5.3em,1.5)
{
0.01
}
;
\node
[anchor=north]
(y1) at ([xshift=0em,yshift=-3.3em]y2.south)
{
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[anchor=north]
(y3) at ([xshift=0em,yshift=4.5em]y2.north)
{
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[anchor=north]
(y4) at ([xshift=0em,yshift=6.6em]y3.north)
{$
\vdots
$}
;
\node
[anchor=north]
(y5) at ([xshift=0em,yshift=2em]y4.north)
{
1.00
}
;
\node
[anchor=north]
(x1) at (1em,-3em)
{$
\lambda
_
1
$}
;
\node
[anchor=north]
(x2) at ([xshift=4.5em,yshift=0em]x1.north)
{$
\lambda
_
2
$}
;
\node
[anchor=north]
(x3) at ([xshift=4em,yshift=-1em]x2.north)
{$
...
$}
;
\node
[anchor=north]
(x4) at ([xshift=5em,yshift=1em]x3.north)
{$
\lambda
_{
M
-
1
}$}
;
\node
[anchor=north]
(x5) at ([xshift=5em,yshift=0em]x4.north)
{$
\lambda
_
M
$}
;
\draw
[decorate,decoration={brace}]
(0,4.7) --(4,4.7) node [xshift=-4em,yshift=1.5em,align=center](label1)
{
M dimensions
}
;
\draw
[decorate,decoration={brace}]
(4.5,4.3) --(4.5,0) node [xshift=2.3em,yshift=5.8em,align=center](label2)
{
Values
}
;
}
\end{scope}
\begin{scope}
[scale=0.55,xshift=3.2in]
{
\tiny
\draw
[step=1,help lines,color=black]
grid (4,4);
\node
[anchor=north]
(y2) at (-5.3em,1.5)
{
0.01
}
;
\node
[anchor=north]
(y1) at ([xshift=0em,yshift=-3.3em]y2.south)
{
0.00
}
;
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[anchor=north]
(y3) at ([xshift=0em,yshift=4.5em]y2.north)
{
0.02
}
;
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[anchor=north]
(y4) at ([xshift=0em,yshift=6.6em]y3.north)
{$
\vdots
$}
;
\node
[anchor=north]
(y5) at ([xshift=0em,yshift=2em]y4.north)
{
1.00
}
;
\node
[anchor=north]
(x1) at (1em,-3em)
{$
\lambda
_
1
$}
;
\node
[anchor=north]
(x2) at ([xshift=4.5em,yshift=0em]x1.north)
{$
\lambda
_
2
$}
;
\node
[anchor=north]
(x3) at ([xshift=4em,yshift=-1em]x2.north)
{$
...
$}
;
\node
[anchor=north]
(x4) at ([xshift=5em,yshift=1em]x3.north)
{$
\lambda
_{
M
-
1
}$}
;
\node
[anchor=north]
(x5) at ([xshift=5em,yshift=0em]x4.north)
{$
\lambda
_
M
$}
;
\draw
[-](n1) (0,4) -- (0,4.4);
\draw
[-](n2) (1,4) -- (1,4.4);
\draw
[-](n3) (2,4) -- (2,4.4);
\draw
[-](n4) (3,4) -- (3,4.4);
\draw
[-](n5) (4,4) -- (4,4.4);
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r11) at (0,1)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r12) at (1,2)
{}
;
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[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r13) at (2,1)
{}
;
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[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r14) at (3,2)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r15) at (4,4)
{}
;
\draw
[-,very thick,blue!50] (0,1) -- (1,2) -- (2,1) -- (3,2) -- (4,4);
\node
[anchor=north]
(p1) at (5.7,4.3)
{
\scriptsize
{$
\leftarrow
$
\textbf
{
path
}
:
}}
;
\node
[anchor=north]
(e1) at ([xshift=0,yshift=-0.4em]p1.south)
{$
w
_
1
=
0
.
01
$}
;
\node
[anchor=north]
(e2) at ([xshift=0,yshift=-0.8em]e1.south)
{$
w
_
2
=
0
.
02
$}
;
\node
[anchor=north]
(e3) at ([xshift=0,yshift=0.4em]e2.south)
{$
\vdots
$}
;
\node
[anchor=north]
(e4) at ([xshift=0,yshift=-0.2em]e3.south)
{$
w
_
M
=
1
.
00
$}
;
}
\end{scope}
\begin{scope}
[scale=0.55,xshift=6.8in]
{
\tiny
\draw
[step=1,help lines,color=black]
grid (4,4);
\node
[anchor=north]
(y2) at (-5.3em,1.5)
{
0.01
}
;
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[anchor=north]
(y1) at ([xshift=0em,yshift=-3.3em]y2.south)
{
0.00
}
;
\node
[anchor=north]
(y1) at ([xshift=0em,yshift=-3.3em]y2.south)
{
0.00
}
;
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[anchor=north]
(y3) at ([xshift=0em,yshift=4.5em]y2.north)
{
0.02
}
;
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[anchor=north]
(y3) at ([xshift=0em,yshift=4.5em]y2.north)
{
0.02
}
;
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[anchor=north]
(y4) at ([xshift=0em,yshift=6.6em]y3.north)
{$
\vdots
$}
;
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[anchor=north]
(y4) at ([xshift=0em,yshift=6.6em]y3.north)
{$
\vdots
$}
;
\node
[anchor=north]
(y5) at ([xshift=0em,yshift=2em]y4.north)
{
1.00
}
;
\node
[anchor=north]
(y5) at ([xshift=0em,yshift=2em]y4.north)
{
1.00
}
;
\node
[anchor=north]
(x1) at (
[xshift=2em,yshift=-3em]n1.south
)
{$
\lambda
_
1
$}
;
\node
[anchor=north]
(x1) at (
1em,-3em
)
{$
\lambda
_
1
$}
;
\node
[anchor=north]
(x2) at ([xshift=4.5em,yshift=0em]x1.north)
{$
\lambda
_
2
$}
;
\node
[anchor=north]
(x2) at ([xshift=4.5em,yshift=0em]x1.north)
{$
\lambda
_
2
$}
;
\node
[anchor=north]
(x3) at ([xshift=4em,yshift=-1em]x2.north)
{$
...
$}
;
\node
[anchor=north]
(x3) at ([xshift=4em,yshift=-1em]x2.north)
{$
...
$}
;
\node
[anchor=north]
(x4) at ([xshift=5em,yshift=1em]x3.north)
{$
\lambda
_{
M
-
1
}$}
;
\node
[anchor=north]
(x4) at ([xshift=5em,yshift=1em]x3.north)
{$
\lambda
_{
M
-
1
}$}
;
...
...
Book/Chapter4/chapter4.tex
查看文件 @
bd87e7ad
...
@@ -701,9 +701,7 @@ dr = start_i-end_{i-1}-1
...
@@ -701,9 +701,7 @@ dr = start_i-end_{i-1}-1
%----------------------------------------------
%----------------------------------------------
\begin{figure}
[htp]
\begin{figure}
[htp]
\centering
\centering
\begin{tabular}
{
l l l
}
\input
{
./Chapter4/Figures/search-space-representation-of-feature-weight
}
&
\subfigure
{
\input
{
./Chapter4/Figures/search-space-representation-of-feature-weight-1
}}
\subfigure
{
\input
{
./Chapter4/Figures/search-space-representation-of-feature-weight-2
}}
\subfigure
{
\input
{
./Chapter4/Figures/search-space-representation-of-feature-weight-3
}}
&
\\
\end{tabular}
\caption
{
特征权重的搜索空间表示
}
\caption
{
特征权重的搜索空间表示
}
\label
{
fig:4-23
}
\label
{
fig:4-23
}
\end{figure}
\end{figure}
...
@@ -716,9 +714,7 @@ dr = start_i-end_{i-1}-1
...
@@ -716,9 +714,7 @@ dr = start_i-end_{i-1}-1
%----------------------------------------------
%----------------------------------------------
\begin{figure}
[htp]
\begin{figure}
[htp]
\centering
\centering
\begin{tabular}
{
l l
}
\input
{
./Chapter4/Figures/grid-search
}
\subfigure
{
\input
{
./Chapter4/Figures/grid-search-1
}}
&
\subfigure
{
\input
{
./Chapter4/Figures/grid-search-2
}}
\\
\end{tabular}
\caption
{
格搜索（左侧：所有点都访问（蓝色）；右侧：避开无效点（绿色））
}
\caption
{
格搜索（左侧：所有点都访问（蓝色）；右侧：避开无效点（绿色））
}
\label
{
fig:4-24
}
\label
{
fig:4-24
}
\end{figure}
\end{figure}
...
...
Book/ChapterAppend/chapterappend.tex
查看文件 @
bd87e7ad
...
@@ -173,7 +173,7 @@
...
@@ -173,7 +173,7 @@
%----------------------------------------------------------------------------------------
%----------------------------------------------------------------------------------------
\section
{
IBM模型3训练方法
}
\section
{
IBM模型3训练方法
}
\parinterval
模型3的参数估计与模型1和模型2采用相同的方法。这里直接给出辅助函数。
\parinterval
IBM
模型3的参数估计与模型1和模型2采用相同的方法。这里直接给出辅助函数。
\begin{eqnarray}
\begin{eqnarray}
h(t,d,n,p,
\lambda
,
\mu
,
\nu
,
\zeta
)
&
=
&
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
s
}
|
\mathbf
{
t
}
)-
\sum
_{
t
}
\lambda
_{
t
}
\big
(
\sum
_{
s
}
t(s|t)-1
\big
)
\nonumber
\\
h(t,d,n,p,
\lambda
,
\mu
,
\nu
,
\zeta
)
&
=
&
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
s
}
|
\mathbf
{
t
}
)-
\sum
_{
t
}
\lambda
_{
t
}
\big
(
\sum
_{
s
}
t(s|t)-1
\big
)
\nonumber
\\
&
&
-
\sum
_{
i
}
\mu
_{
iml
}
\big
(
\sum
_{
j
}
d(j|i,m,l)-1
\big
)
\nonumber
\\
&
&
-
\sum
_{
i
}
\mu
_{
iml
}
\big
(
\sum
_{
j
}
d(j|i,m,l)-1
\big
)
\nonumber
\\
...
@@ -181,7 +181,7 @@ h(t,d,n,p, \lambda,\mu, \nu, \zeta) & = & \textrm{P}_{\theta}(\mathbf{s}|\mathb
...
@@ -181,7 +181,7 @@ h(t,d,n,p, \lambda,\mu, \nu, \zeta) & = & \textrm{P}_{\theta}(\mathbf{s}|\mathb
\label
{
eq:1.1
}
\label
{
eq:1.1
}
\end{eqnarray}
\end{eqnarray}
\parinterval
由于篇幅所限这里略去了推导步骤直接给出
一些用于参数估计的等
式。
\parinterval
由于篇幅所限这里略去了推导步骤直接给出
具体公
式。
\begin{eqnarray}
\begin{eqnarray}
c(s|t,
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
\mathbf
{
a
}}
\big
[\textrm{P}_{\theta}(\mathbf{s},\mathbf{a}|\mathbf{t}) \times \sum_{j=1}^{m} (\delta(s_j,s) \cdot \delta(t_{a_{j}},t))\big]
\label
{
eq:1.2
}
\\
c(s|t,
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
\mathbf
{
a
}}
\big
[\textrm{P}_{\theta}(\mathbf{s},\mathbf{a}|\mathbf{t}) \times \sum_{j=1}^{m} (\delta(s_j,s) \cdot \delta(t_{a_{j}},t))\big]
\label
{
eq:1.2
}
\\
c(j|i,m,l;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
\mathbf
{
a
}}
\big
[\textrm{P}_{\theta}(\mathbf{s},\mathbf{a}|\mathbf{t}) \times \delta(i,a_j)\big]
\label
{
eq:1.3
}
\\
c(j|i,m,l;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
\mathbf
{
a
}}
\big
[\textrm{P}_{\theta}(\mathbf{s},\mathbf{a}|\mathbf{t}) \times \delta(i,a_j)\big]
\label
{
eq:1.3
}
\\
...
@@ -202,9 +202,9 @@ n(\varphi|t) & = & \nu_{t}^{-1} \times \sum_{s=1}^{K}c(\varphi |t;\mathbf{s}^{[k
...
@@ -202,9 +202,9 @@ n(\varphi|t) & = & \nu_{t}^{-1} \times \sum_{s=1}^{K}c(\varphi |t;\mathbf{s}^{[k
p
_
x
&
=
&
\zeta
^{
-1
}
\sum
_{
k=1
}^{
K
}
c(x;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
\label
{
eq:1.10
}
p
_
x
&
=
&
\zeta
^{
-1
}
\sum
_{
k=1
}^{
K
}
c(x;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
\label
{
eq:1.10
}
\end{eqnarray}
\end{eqnarray}
\parinterval
在模型3中，因为
产出率的引入，并不能像模型1和模型2那样，在保证正确性的情况下加速参数估计的过程。这就使得每次迭代过程中，都不得不面对大小为
$
(
l
+
1
)
^
m
$
的词对齐空间。遍历所有
$
(
l
+
1
)
^
m
$
个词对齐所带来的高时间复杂度显然是不能被接受的。因此就要考虑能否仅利用词对齐空间中的部分词对齐对这些参数进行估计。比较简单且直接的方法就是仅利用Viterbi对齐来进行参数估计
\footnote
{
Viterbi词对齐可以被简单的看作搜索到的最好词对齐。
}
。 遗憾的是，在模型3中并没有方法直接获得Viterbi对齐。这样只能采用一种折中的策略，即仅考虑那些使得
$
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
s
}
,
\mathbf
{
a
}
|
\mathbf
{
t
}
)
$
达到较高值的词对齐。这里把这部分词对齐组成的集合记为
$
S
$
。式
\ref
{
eq:1.2
}
可以被修改为：
\parinterval
在模型3中，因为
繁衍率的引入，并不能像模型1和模型2那样，在保证正确性的情况下加速参数估计的过程。这就使得每次迭代过程中，都不得不面对大小为
$
(
l
+
1
)
^
m
$
的词对齐空间。遍历所有
$
(
l
+
1
)
^
m
$
个词对齐所带来的高时间复杂度显然是不能被接受的。因此就要考虑能否仅利用词对齐空间中的部分词对齐对这些参数进行估计。比较简单的方法是仅使用Viterbi对齐来进行参数估计，这里Viterbi 词对齐可以被简单的看作搜索到的最好词对齐。遗憾的是，在模型3中并没有方法直接获得Viterbi对齐。这样只能采用一种折中的策略，即仅考虑那些使得
$
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
s
}
,
\mathbf
{
a
}
|
\mathbf
{
t
}
)
$
达到较高值的词对齐。这里把这部分词对齐组成的集合记为
$
S
$
。式
\ref
{
eq:1.2
}
可以被修改为：
\begin{eqnarray}
\begin{eqnarray}
c(s|t,
\mathbf
{
s
}
,
\mathbf
{
t
}
)
\approx
\sum
_{
\mathbf
{
a
}
\in
\mathbf
{
S
}
}
\big
[\textrm{P}_{\theta}(\mathbf{s},\mathbf{a}|\mathbf{t}) \times \sum_{j=1}^{m}(\delta(s_j,\mathbf{s}) \cdot \delta(t_{a_{j}},\mathbf{t})) \big]
c(s|t,
\mathbf
{
s
}
,
\mathbf
{
t
}
)
\approx
\sum
_{
\mathbf
{
a
}
\in
S
}
\big
[\textrm{P}_{\theta}(\mathbf{s},\mathbf{a}|\mathbf{t}) \times \sum_{j=1}^{m}(\delta(s_j,\mathbf{s}) \cdot \delta(t_{a_{j}},\mathbf{t})) \big]
\label
{
eq:1.11
}
\label
{
eq:1.11
}
\end{eqnarray}
\end{eqnarray}
...
@@ -222,7 +222,7 @@ S = N(b^{\infty}(V(\mathbf{s}|\mathbf{t};2))) \cup (\mathop{\cup}\limits_{ij} N(
...
@@ -222,7 +222,7 @@ S = N(b^{\infty}(V(\mathbf{s}|\mathbf{t};2))) \cup (\mathop{\cup}\limits_{ij} N(
\end{itemize}
\end{itemize}
\vspace
{
0.5em
}
\vspace
{
0.5em
}
\parinterval
公式
\ref
{
eq:1.12
}
中，
$
b
^{
\infty
}
(
V
(
\mathbf
{
s
}
|
\mathbf
{
t
}
;
2
))
$
和
$
b
_{
i
\leftrightarrow
j
}^{
\infty
}
(
V
_{
i
\leftrightarrow
j
}
(
\mathbf
{
s
}
|
\mathbf
{
t
}
,
2
))
$
分别是对
$
V
(
\mathbf
{
s
}
|
\mathbf
{
t
}
;
3
)
$
和
$
V
_{
i
\leftrightarrow
j
}
(
\mathbf
{
s
}
|
\mathbf
{
t
}
,
3
)
$
的估计。在计算
$
S
$
的过程中，需要知道一个对齐
$
\bf
{
a
}$
的邻居
$
\bf
{
a
}^{
'
}$
的概率，即通过
$
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
,
\mathbf
{
s
}
|
\mathbf
{
t
}
)
$
计算
$
\textrm
{
p
}_{
\theta
}
(
\mathbf
{
a
}
',
\mathbf
{
s
}
|
\mathbf
{
t
}
)
$
。在模型3中，如果
$
\bf
{
a
}$
和
$
\bf
{
a
}
'
$
仅区别于某个源语单词对齐到的目标位置上（
$
a
_
j
\neq
a
_{
j
}
'
$
），那么
\parinterval
公式
\ref
{
eq:1.12
}
中，
$
b
^{
\infty
}
(
V
(
\mathbf
{
s
}
|
\mathbf
{
t
}
;
2
))
$
和
$
b
_{
i
\leftrightarrow
j
}^{
\infty
}
(
V
_{
i
\leftrightarrow
j
}
(
\mathbf
{
s
}
|
\mathbf
{
t
}
,
2
))
$
分别是对
$
V
(
\mathbf
{
s
}
|
\mathbf
{
t
}
;
3
)
$
和
$
V
_{
i
\leftrightarrow
j
}
(
\mathbf
{
s
}
|
\mathbf
{
t
}
,
3
)
$
的估计。在计算
$
S
$
的过程中，需要知道一个对齐
$
\bf
{
a
}$
的邻居
$
\bf
{
a
}^{
'
}$
的概率，即通过
$
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
,
\mathbf
{
s
}
|
\mathbf
{
t
}
)
$
计算
$
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
',
\mathbf
{
s
}
|
\mathbf
{
t
}
)
$
。在模型3中，如果
$
\bf
{
a
}$
和
$
\bf
{
a
}
'
$
仅区别于某个源语单词对齐到的目标位置上（
$
a
_
j
\neq
a
_{
j
}
'
$
），那么
\begin{eqnarray}
\begin{eqnarray}
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
',
\mathbf
{
s
}
|
\mathbf
{
t
}
)
&
=
&
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
,
\mathbf
{
s
}
|
\mathbf
{
t
}
)
\cdot
\nonumber
\\
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
',
\mathbf
{
s
}
|
\mathbf
{
t
}
)
&
=
&
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
,
\mathbf
{
s
}
|
\mathbf
{
t
}
)
\cdot
\nonumber
\\
...
@@ -247,7 +247,7 @@ S = N(b^{\infty}(V(\mathbf{s}|\mathbf{t};2))) \cup (\mathop{\cup}\limits_{ij} N(
...
@@ -247,7 +247,7 @@ S = N(b^{\infty}(V(\mathbf{s}|\mathbf{t};2))) \cup (\mathop{\cup}\limits_{ij} N(
\parinterval
模型4的参数估计基本与模型3一致。需要修改的是扭曲度的估计公式，对于目标语第
$
i
$
个cept.生成的第一单词，可以得到（假设有
$
K
$
个训练样本）：
\parinterval
模型4的参数估计基本与模型3一致。需要修改的是扭曲度的估计公式，对于目标语第
$
i
$
个cept.生成的第一单词，可以得到（假设有
$
K
$
个训练样本）：
\begin{eqnarray}
\begin{eqnarray}
d
_
1(
\Delta
_
j|ca,cb
;
\mathbf
{
s
}
,
\mathbf
{
t
}
) =
\mu
_{
1cacb
}^{
-1
}
\times
\sum
_{
k=1
}^{
K
}
c
_
1(
\Delta
_
j|ca,cb;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
d
_
1(
\Delta
_
j|ca,cb) =
\mu
_{
1cacb
}^{
-1
}
\times
\sum
_{
k=1
}^{
K
}
c
_
1(
\Delta
_
j|ca,cb;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
\label
{
eq:1.15
}
\label
{
eq:1.15
}
\end{eqnarray}
\end{eqnarray}
...
@@ -255,7 +255,7 @@ d_1(\Delta_j|ca,cb;\mathbf{s},\mathbf{t}) = \mu_{1cacb}^{-1} \times \sum_{k=1}^{
...
@@ -255,7 +255,7 @@ d_1(\Delta_j|ca,cb;\mathbf{s},\mathbf{t}) = \mu_{1cacb}^{-1} \times \sum_{k=1}^{
\begin{eqnarray}
\begin{eqnarray}
c
_
1(
\Delta
_
j|ca,cb;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
\mathbf
{
a
}}
\big
[\textrm{P}_{\theta}(\mathbf{s},\mathbf{a}|\mathbf{t}) \times s_1(\Delta_j|ca,cb;\mathbf{a},\mathbf{s},\mathbf{t})\big]
\label
{
eq:1.16
}
\\
c
_
1(
\Delta
_
j|ca,cb;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
\mathbf
{
a
}}
\big
[\textrm{P}_{\theta}(\mathbf{s},\mathbf{a}|\mathbf{t}) \times s_1(\Delta_j|ca,cb;\mathbf{a},\mathbf{s},\mathbf{t})\big]
\label
{
eq:1.16
}
\\
s
_
1(
\Delta
_
j|ca,cb;
\rm
{
a
}
,
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
i=1
}^
l
\big
[
\varepsilon
(
\phi
_
i)
\cdot
\delta
(
\pi
_{
i1
}
-
\odot
_{
i
}
,
\Delta
_
j)
\cdot
\nonumber
\\
s
_
1(
\Delta
_
j|ca,cb;
\rm
{
a
}
,
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
i=1
}^
l
\big
[
\varepsilon
(
\
var
phi
_
i)
\cdot
\delta
(
\pi
_{
i1
}
-
\odot
_{
i
}
,
\Delta
_
j)
\cdot
\nonumber
\\
&
&
\delta
(A(t
_{
i-1
}
),ca)
\cdot
\delta
(B(
\tau
_{
i1
}
),cb)
\big
]
\label
{
eq:1.17
}
&
&
\delta
(A(t
_{
i-1
}
),ca)
\cdot
\delta
(B(
\tau
_{
i1
}
),cb)
\big
]
\label
{
eq:1.17
}
\end{eqnarray}
\end{eqnarray}
...
@@ -272,7 +272,7 @@ s_1(\Delta_j|ca,cb;\rm{a},\mathbf{s},\mathbf{t}) & = & \sum_{i=1}^l \big[\vareps
...
@@ -272,7 +272,7 @@ s_1(\Delta_j|ca,cb;\rm{a},\mathbf{s},\mathbf{t}) & = & \sum_{i=1}^l \big[\vareps
对于目标语第
$
i
$
个cept.生成的其他单词（非第一个单词），可以得到：
对于目标语第
$
i
$
个cept.生成的其他单词（非第一个单词），可以得到：
\begin{eqnarray}
\begin{eqnarray}
d
_{
>1
}
(
\Delta
_
j|cb
;
\mathbf
{
s
}
,
\mathbf
{
t
}
) =
\mu
_{
>1cb
}^{
-1
}
\times
\sum
_{
k=1
}^{
K
}
c
_{
>1
}
(
\Delta
_
j|cb;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
d
_{
>1
}
(
\Delta
_
j|cb) =
\mu
_{
>1cb
}^{
-1
}
\times
\sum
_{
k=1
}^{
K
}
c
_{
>1
}
(
\Delta
_
j|cb;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
\label
{
eq:1.18
}
\label
{
eq:1.18
}
\end{eqnarray}
\end{eqnarray}
...
@@ -280,7 +280,7 @@ d_{>1}(\Delta_j|cb;\mathbf{s},\mathbf{t}) = \mu_{>1cb}^{-1} \times \sum_{k=1}^{K
...
@@ -280,7 +280,7 @@ d_{>1}(\Delta_j|cb;\mathbf{s},\mathbf{t}) = \mu_{>1cb}^{-1} \times \sum_{k=1}^{K
\begin{eqnarray}
\begin{eqnarray}
c
_{
>1
}
(
\Delta
_
j|cb;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
\mathbf
{
a
}}
\big
[\textrm{p}_{\theta}(\mathbf{s},\mathbf{a}|\mathbf{t}) \times s_{>1}(\Delta_j|cb;\mathbf{a},\mathbf{s},\mathbf{t}) \big]
\label
{
eq:1.19
}
\\
c
_{
>1
}
(
\Delta
_
j|cb;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
\mathbf
{
a
}}
\big
[\textrm{p}_{\theta}(\mathbf{s},\mathbf{a}|\mathbf{t}) \times s_{>1}(\Delta_j|cb;\mathbf{a},\mathbf{s},\mathbf{t}) \big]
\label
{
eq:1.19
}
\\
s
_{
>1
}
(
\Delta
_
j|cb;
\mathbf
{
a
}
,
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
i=1
}^
l
\big
[\varepsilon(\
phi_i-1)\sum_{k=2}^{\
phi_i}\delta(\pi_{[i]
k
}
-
\pi
_{
[i]k-1
}
,
\Delta
_
j)
\cdot
\nonumber
ß
\\
s
_{
>1
}
(
\Delta
_
j|cb;
\mathbf
{
a
}
,
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
i=1
}^
l
\big
[\varepsilon(\
varphi_i-1)\sum_{k=2}^{\var
phi_i}\delta(\pi_{[i]
k
}
-
\pi
_{
[i]k-1
}
,
\Delta
_
j)
\cdot
\nonumber
ß
\\
&
&
\delta
(B(
\tau
_{
[i]k
}
),cb)
\big
]
\label
{
eq:1.20
}
&
&
\delta
(B(
\tau
_{
[i]k
}
),cb)
\big
]
\label
{
eq:1.20
}
\end{eqnarray}
\end{eqnarray}
...
@@ -291,7 +291,7 @@ s_{>1}(\Delta_j|cb;\mathbf{a},\mathbf{s},\mathbf{t}) & = & \sum_{i=1}^l \big[\va
...
@@ -291,7 +291,7 @@ s_{>1}(\Delta_j|cb;\mathbf{a},\mathbf{s},\mathbf{t}) & = & \sum_{i=1}^l \big[\va
\label
{
eq:1.22
}
\label
{
eq:1.22
}
\end{eqnarray}
\end{eqnarray}
\parinterval
对于一个对齐
$
\mathbf
{
a
}$
，可用模型3对它的邻居进行排名，即按
$
\textrm
{
P
}_{
\theta
}
(
b
(
\mathbf
{
a
}
)
|
\mathbf
{
s
}
,
\mathbf
{
t
}
;
3
)
$
排序，其中
$
b
(
\mathbf
{
a
}
)
$
表示
$
\mathbf
{
a
}$
的邻居。
$
\tilde
{
b
}
(
\mathbf
{
a
}
)
$
表示这个排名表中满足
$
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
'|
\mathbf
{
s
}
,
\mathbf
{
t
}
;
4
)
>
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
|
\mathbf
{
s
}
,
\mathbf
{
t
}
;
4
)
$
的最高排名的
$
\mathbf
{
a
}
'
$
。
同理可知
$
\tilde
{
b
}_{
i
\leftrightarrow
j
}^{
\infty
}
(
\mathbf
{
a
}
)
$
的意义。这里之所以不用模型3中采用的方法直接利用
$
b
^{
\infty
}
(
\mathbf
{
a
}
)
$
得到模型4中高概率的对齐，是因为模型4中，要想获得某个对齐
$
\mathbf
{
a
}$
的邻居
$
\mathbf
{
a
}
'
$
，
必须做很大调整，比如：调整
$
\tau
_{
[
i
]
1
}$
和
$
\odot
_{
i
}$
等等。这个过程要比模型3的相应过程复杂得多。因此在模型4中只能借助于模型3的中间步骤来进行参数估计。
\parinterval
对于一个对齐
$
\mathbf
{
a
}$
，可用模型3对它的邻居进行排名，即按
$
\textrm
{
P
}_{
\theta
}
(
b
(
\mathbf
{
a
}
)
|
\mathbf
{
s
}
,
\mathbf
{
t
}
;
3
)
$
排序，其中
$
b
(
\mathbf
{
a
}
)
$
表示
$
\mathbf
{
a
}$
的邻居。
$
\tilde
{
b
}
(
\mathbf
{
a
}
)
$
表示这个排名表中满足
$
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
'|
\mathbf
{
s
}
,
\mathbf
{
t
}
;
4
)
>
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
|
\mathbf
{
s
}
,
\mathbf
{
t
}
;
4
)
$
的最高排名的
$
\mathbf
{
a
}
'
$
。
同理可知
$
\tilde
{
b
}_{
i
\leftrightarrow
j
}^{
\infty
}
(
\mathbf
{
a
}
)
$
的意义。这里之所以不用模型3中采用的方法直接利用
$
b
^{
\infty
}
(
\mathbf
{
a
}
)
$
得到模型4中高概率的对齐，是因为模型4中要想获得某个对齐
$
\mathbf
{
a
}$
的邻居
$
\mathbf
{
a
}
'
$
必须做很大调整，比如：调整
$
\tau
_{
[
i
]
1
}$
和
$
\odot
_{
i
}$
等等。这个过程要比模型3的相应过程复杂得多。因此在模型4中只能借助于模型3的中间步骤来进行参数估计。
\setlength
{
\belowdisplayskip
}{
3pt
}
%调整空白大小
\setlength
{
\belowdisplayskip
}{
3pt
}
%调整空白大小
%----------------------------------------------------------------------------------------
%----------------------------------------------------------------------------------------
...
@@ -299,10 +299,10 @@ s_{>1}(\Delta_j|cb;\mathbf{a},\mathbf{s},\mathbf{t}) & = & \sum_{i=1}^l \big[\va
...
@@ -299,10 +299,10 @@ s_{>1}(\Delta_j|cb;\mathbf{a},\mathbf{s},\mathbf{t}) & = & \sum_{i=1}^l \big[\va
%----------------------------------------------------------------------------------------
%----------------------------------------------------------------------------------------
\section
{
IBM模型5训练方法
}
\section
{
IBM模型5训练方法
}
\parinterval
模型5的参数估计过程也
与模型3
的过程基本一致，二者的区别在于扭曲度的估计公式。在模型5中，对于目标语第
$
i
$
个cept.生成的第一单词，可以得到（假设有
$
K
$
个训练样本）：
\parinterval
模型5的参数估计过程也
模型4
的过程基本一致，二者的区别在于扭曲度的估计公式。在模型5中，对于目标语第
$
i
$
个cept.生成的第一单词，可以得到（假设有
$
K
$
个训练样本）：
\begin{eqnarray}
\begin{eqnarray}
d
_
1(
\Delta
_
j|cb
;
\mathbf
{
s
}
,
\mathbf
{
t
}
) =
\mu
_{
1cb
}^{
-1
}
\times
\sum
_{
k=1
}^{
K
}
c
_
1(
\Delta
_
j|cb;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
d
_
1(
\Delta
_
j|cb) =
\mu
_{
1cb
}^{
-1
}
\times
\sum
_{
k=1
}^{
K
}
c
_
1(
\Delta
_
j|cb;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
\label
{
eq:1.23
}
\label
{
eq:1.23
}
\end{eqnarray}
\end{eqnarray}
...
@@ -310,15 +310,15 @@ d_1(\Delta_j|cb;\mathbf{s},\mathbf{t}) = \mu_{1cb}^{-1} \times \sum_{k=1}^{K}c_1
...
@@ -310,15 +310,15 @@ d_1(\Delta_j|cb;\mathbf{s},\mathbf{t}) = \mu_{1cb}^{-1} \times \sum_{k=1}^{K}c_1
\begin{eqnarray}
\begin{eqnarray}
c
_
1(
\Delta
_
j|cb,v
_
x,v
_
y;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
\mathbf
{
a
}}
\Big
[ \textrm{P}(\mathbf{s},\mathbf{a}|\mathbf{t}) \times s_1(\Delta_j|cb,v_x,v_y;\mathbf{a},\mathbf{s},\mathbf{t}) \Big]
\label
{
eq:1.24
}
\\
c
_
1(
\Delta
_
j|cb,v
_
x,v
_
y;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
\mathbf
{
a
}}
\Big
[ \textrm{P}(\mathbf{s},\mathbf{a}|\mathbf{t}) \times s_1(\Delta_j|cb,v_x,v_y;\mathbf{a},\mathbf{s},\mathbf{t}) \Big]
\label
{
eq:1.24
}
\\
s
_
1(
\Delta
_
j|cb,v
_
x,v
_
y;
\mathbf
{
a
}
,
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
i=1
}^
l
\Big
[
\varepsilon
(
\phi
_
i)
\cdot
\delta
(v
_{
\pi
_{
i1
}}
,
\Delta
_
j)
\cdot
\delta
(v
_{
\odot
_{
i-1
}}
,v
_
x)
\nonumber
\\
s
_
1(
\Delta
_
j|cb,v
_
x,v
_
y;
\mathbf
{
a
}
,
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
i=1
}^
l
\Big
[
\varepsilon
(
\
var
phi
_
i)
\cdot
\delta
(v
_{
\pi
_{
i1
}}
,
\Delta
_
j)
\cdot
\delta
(v
_{
\odot
_{
i-1
}}
,v
_
x)
\nonumber
\\
&
&
\cdot
\delta
(v
_
m-
\phi
_
i+1,v
_
y)
\cdot
\delta
(v
_{
\pi
_{
i1
}}
,v
_{
\pi
_{
i1
}
-1
}
)
\Big
]
\label
{
eq:1.25
}
&
&
\cdot
\delta
(v
_
m-
\
var
phi
_
i+1,v
_
y)
\cdot
\delta
(v
_{
\pi
_{
i1
}}
,v
_{
\pi
_{
i1
}
-1
}
)
\Big
]
\label
{
eq:1.25
}
\end{eqnarray}
\end{eqnarray}
对于目标语第
$
i
$
个cept.生成的其他单词（非第一个单词），可以得到：
对于目标语第
$
i
$
个cept.生成的其他单词（非第一个单词），可以得到：
\begin{eqnarray}
\begin{eqnarray}
d
_{
>1
}
(
\Delta
_
j|cb,v
;
\mathbf
{
s
}
,
\mathbf
{
t
}
) =
\mu
_{
>1cb
}^{
-1
}
\times
\sum
_{
k=1
}^{
K
}
c
_{
>1
}
(
\Delta
_
j|cb,v;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
d
_{
>1
}
(
\Delta
_
j|cb,v) =
\mu
_{
>1cb
}^{
-1
}
\times
\sum
_{
k=1
}^{
K
}
c
_{
>1
}
(
\Delta
_
j|cb,v;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
\label
{
eq:1.26
}
\label
{
eq:1.26
}
\end{eqnarray}
\end{eqnarray}
...
@@ -326,18 +326,18 @@ d_{>1}(\Delta_j|cb,v;\mathbf{s},\mathbf{t}) = \mu_{>1cb}^{-1} \times \sum_{k=1}^
...
@@ -326,18 +326,18 @@ d_{>1}(\Delta_j|cb,v;\mathbf{s},\mathbf{t}) = \mu_{>1cb}^{-1} \times \sum_{k=1}^
\begin{eqnarray}
\begin{eqnarray}
c
_{
>1
}
(
\Delta
_
j|cb,v;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
\mathbf
{
a
}}
\Big
[\textrm{P}(\mathbf{a},\mathbf{s}|\mathbf{t}) \times s_{>1}(\Delta_j|cb,v;\mathbf{a},\mathbf{s},\mathbf{t}) \Big]
\label
{
eq:1.27
}
\\
c
_{
>1
}
(
\Delta
_
j|cb,v;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
\mathbf
{
a
}}
\Big
[\textrm{P}(\mathbf{a},\mathbf{s}|\mathbf{t}) \times s_{>1}(\Delta_j|cb,v;\mathbf{a},\mathbf{s},\mathbf{t}) \Big]
\label
{
eq:1.27
}
\\
s
_{
>1
}
(
\Delta
_
j|cb,v;
\mathbf
{
a
}
,
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
i=1
}^
l
\Big
[\varepsilon(\
phi_i-1)\sum_{k=2}^{\
phi_i} \big[\delta(v_{\pi_{ik}}-v_{\pi_{[i]
k
}
-1
}
,
\Delta
_
j)
\nonumber
\\
s
_{
>1
}
(
\Delta
_
j|cb,v;
\mathbf
{
a
}
,
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\sum
_{
i=1
}^
l
\Big
[\varepsilon(\
varphi_i-1)\sum_{k=2}^{\var
phi_i} \big[\delta(v_{\pi_{ik}}-v_{\pi_{[i]
k
}
-1
}
,
\Delta
_
j)
\nonumber
\\
&
&
\cdot
\delta
(B(
\tau
_{
[i]k
}
) ,cb)
\cdot
\delta
(v
_
m-v
_{
\pi
_{
i(k-1)
}}
-
\phi
_
i+k,v)
\nonumber
\\
&
&
\cdot
\delta
(B(
\tau
_{
[i]k
}
) ,cb)
\cdot
\delta
(v
_
m-v
_{
\pi
_{
i(k-1)
}}
-
\
var
phi
_
i+k,v)
\nonumber
\\
&
&
\cdot
\delta
(v
_{
\pi
_{
i1
}}
,v
_{
\pi
_{
i1
}
-1
}
)
\big
]
\Big
]
\label
{
eq:1.28
}
&
&
\cdot
\delta
(v
_{
\pi
_{
i1
}}
,v
_{
\pi
_{
i1
}
-1
}
)
\big
]
\Big
]
\label
{
eq:1.28
}
\end{eqnarray}
\end{eqnarray}
\vspace
{
0.5em
}
\vspace
{
0.5em
}
\parinterval
从式
(
\ref
{
eq:1.24
}
)中可以看出因子
$
\delta
(
v
_{
\pi
_{
i
1
}}
,v
_{
\pi
_{
i
1
}
-
1
}
)
$
保证了，即使对齐
$
\mathbf
{
a
}$
不合理（一个源语位置对应多个目标语
位置）也可以避免在这个不合理的对齐上计算结果。需要注意的是因子
$
\delta
(
v
_{
\pi
_{
p
1
}}
,v
_{
\pi
_{
p
1
-
1
}}
)
$
，确保了
$
\mathbf
{
a
}$
中不合理的部分不产生坏的影响，而
$
\mathbf
{
a
}$
中其他正确的部分仍会参与迭代。
\parinterval
从式
\ref
{
eq:1.24
}
中可以看出因子
$
\delta
(
v
_{
\pi
_{
i
1
}}
,v
_{
\pi
_{
i
1
}
-
1
}
)
$
保证了，即使对齐
$
\mathbf
{
a
}$
不合理（一个源语言位置对应多个目标语言
位置）也可以避免在这个不合理的对齐上计算结果。需要注意的是因子
$
\delta
(
v
_{
\pi
_{
p
1
}}
,v
_{
\pi
_{
p
1
-
1
}}
)
$
，确保了
$
\mathbf
{
a
}$
中不合理的部分不产生坏的影响，而
$
\mathbf
{
a
}$
中其他正确的部分仍会参与迭代。
\parinterval
不过上面的参数估计过程与IBM前4个模型的参数估计过程并不完全一样。IBM前4个模型在每次迭代中，可以在给定
$
\mathbf
{
s
}$
、
$
\mathbf
{
t
}$
和一个对齐
$
\mathbf
{
a
}$
的情况下直接计算并更新参数。但是在模型5的参数估计过程中（如公式
\ref
{
eq:1.24
}
），需要模拟出由
$
\mathbf
{
t
}$
生成
$
\mathbf
{
s
}$
的过程才能得到正确的结果，因为从
$
\mathbf
{
t
}$
、
$
\mathbf
{
s
}$
和
$
\mathbf
{
a
}$
中是不能直接得到 的正确结果的。具体说，就是要从目标语言句子的第一个单词开始到最后一个单词结束，依次生成每个目标语言单词对应的源语言单词，每处理完一个目标语言单词就要暂停，然后才能计算式
\ref
{
eq:1.24
}
中求和符号里面的内容。这也就是说即使给定了
$
\mathbf
{
s
}$
、
$
\mathbf
{
t
}$
和一个对齐
$
\mathbf
{
a
}$
，也不能直接在它们上进行计算，必须重新模拟
$
\mathbf
{
t
}$
到
$
\mathbf
{
s
}$
的生成过程。
\parinterval
不过上面的参数估计过程与IBM前4个模型的参数估计过程并不完全一样。IBM前4个模型在每次迭代中，可以在给定
$
\mathbf
{
s
}$
、
$
\mathbf
{
t
}$
和一个对齐
$
\mathbf
{
a
}$
的情况下直接计算并更新参数。但是在模型5的参数估计过程中（如公式
\ref
{
eq:1.24
}
），需要模拟出由
$
\mathbf
{
t
}$
生成
$
\mathbf
{
s
}$
的过程才能得到正确的结果，因为从
$
\mathbf
{
t
}$
、
$
\mathbf
{
s
}$
和
$
\mathbf
{
a
}$
中是不能直接得到 的正确结果的。具体说，就是要从目标语言句子的第一个单词开始到最后一个单词结束，依次生成每个目标语言单词对应的源语言单词，每处理完一个目标语言单词就要暂停，然后才能计算式
\ref
{
eq:1.24
}
中求和符号里面的内容。这也就是说即使给定了
$
\mathbf
{
s
}$
、
$
\mathbf
{
t
}$
和一个对齐
$
\mathbf
{
a
}$
，也不能直接在它们上进行计算，必须重新模拟
$
\mathbf
{
t
}$
到
$
\mathbf
{
s
}$
的生成过程。
\parinterval
从前面的分析可以看出，虽然模型5比模型4更精确，但是模型5过于复杂以至于给参数估计增加了计算量（对于每组
$
\mathbf
{
t
}$
、
$
\mathbf
{
s
}$
和
$
\mathbf
{
a
}$
都要模拟
$
\mathbf
{
t
}$
生成
$
\mathbf
{
s
}$
的翻译过程）。因此模型5的
开发对于
系统实现是一个挑战。
\parinterval
从前面的分析可以看出，虽然模型5比模型4更精确，但是模型5过于复杂以至于给参数估计增加了计算量（对于每组
$
\mathbf
{
t
}$
、
$
\mathbf
{
s
}$
和
$
\mathbf
{
a
}$
都要模拟
$
\mathbf
{
t
}$
生成
$
\mathbf
{
s
}$
的翻译过程）。因此模型5的系统实现是一个挑战。
\parinterval
在模型5中同样需要定义一个词对齐集合
$
S
$
，使得每次迭代都在
$
S
$
上进行。可以对
$
S
$
进行如下定义
\parinterval
在模型5中同样需要定义一个词对齐集合
$
S
$
，使得每次迭代都在
$
S
$
上进行。可以对
$
S
$
进行如下定义
\begin{eqnarray}
\begin{eqnarray}
...
@@ -346,7 +346,7 @@ s_{>1}(\Delta_j|cb,v;\mathbf{a},\mathbf{s},\mathbf{t}) & = & \sum_{i=1}^l\Big[\v
...
@@ -346,7 +346,7 @@ s_{>1}(\Delta_j|cb,v;\mathbf{a},\mathbf{s},\mathbf{t}) & = & \sum_{i=1}^l\Big[\v
\end{eqnarray}
\end{eqnarray}
\vspace
{
0.5em
}
\vspace
{
0.5em
}
\
parinterval
这里
$
\tilde
{
\tilde
{
b
}}
(
\mathbf
{
a
}
)
$
借用了模型4中
$
\tilde
{
b
}
(
\mathbf
{
a
}
)
$
的概念。不过
$
\tilde
{
\tilde
{
b
}}
(
\mathbf
{
a
}
)
$
表示在利用模型3进行排名的列表中满足
$
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
'|
\mathbf
{
s
}
,
\mathbf
{
t
}
;
5
)
$
的最高排名的词对齐
。
\
noindent
其中，
$
\tilde
{
\tilde
{
b
}}
(
\mathbf
{
a
}
)
$
借用了模型4中
$
\tilde
{
b
}
(
\mathbf
{
a
}
)
$
的概念。不过
$
\tilde
{
\tilde
{
b
}}
(
\mathbf
{
a
}
)
$
表示在利用模型3进行排名的列表中满足
$
\textrm
{
P
}_{
\theta
}
(
\mathbf
{
a
}
'|
\mathbf
{
s
}
,
\mathbf
{
t
}
;
5
)
$
的最高排名的词对齐，这里
$
\mathbf
{
a
}
'
$
表示
$
\mathbf
{
a
}$
的邻居
。
\end{appendices}
\end{appendices}
...
...
Book/mt-book-xelatex.idx
查看文件 @
bd87e7ad
...
@@ -7,14 +7,14 @@
...
@@ -7,14 +7,14 @@
\indexentry{数据驱动|hyperpage}{23}
\indexentry{数据驱动|hyperpage}{23}
\indexentry{Data-Driven|hyperpage}{23}
\indexentry{Data-Driven|hyperpage}{23}
\indexentry{编码器-解码器|hyperpage}{30}
\indexentry{编码器-解码器|hyperpage}{30}
\indexentry{
encoder-d
ecoder|hyperpage}{30}
\indexentry{
Encoder-D
ecoder|hyperpage}{30}
\indexentry{质量评价|hyperpage}{32}
\indexentry{质量评价|hyperpage}{32}
\indexentry{Quality Evaluation|hyperpage}{32}
\indexentry{Quality Evaluation|hyperpage}{32}
\indexentry{无参考答案的评价|hyperpage}{32}
\indexentry{无参考答案的评价|hyperpage}{32}
\indexentry{Quality Estimation|hyperpage}{32}
\indexentry{Quality Estimation|hyperpage}{32}
\indexentry{$n$元语法单元|hyperpage}{33}
\indexentry{$n$元语法单元|hyperpage}{33}
\indexentry{$n$-gram准确率|hyperpage}{3
4
}
\indexentry{$n$-gram准确率|hyperpage}{3
3
}
\indexentry{$n$-gram Precision|hyperpage}{3
4
}
\indexentry{$n$-gram Precision|hyperpage}{3
3
}
\indexentry{短句惩罚因子|hyperpage}{34}
\indexentry{短句惩罚因子|hyperpage}{34}
\indexentry{Brevity Penalty|hyperpage}{34}
\indexentry{Brevity Penalty|hyperpage}{34}
\indexentry{分词|hyperpage}{50}
\indexentry{分词|hyperpage}{50}
...
@@ -115,10 +115,10 @@
...
@@ -115,10 +115,10 @@
\indexentry{Disambiguation|hyperpage}{79}
\indexentry{Disambiguation|hyperpage}{79}
\indexentry{最左优先推导|hyperpage}{79}
\indexentry{最左优先推导|hyperpage}{79}
\indexentry{Left-most Derivation|hyperpage}{79}
\indexentry{Left-most Derivation|hyperpage}{79}
\indexentry{概率上下文无关文法|hyperpage}{8
1
}
\indexentry{概率上下文无关文法|hyperpage}{8
0
}
\indexentry{Probabilistic Context-Free Grammar|hyperpage}{8
1
}
\indexentry{Probabilistic Context-Free Grammar|hyperpage}{8
0
}
\indexentry{树库|hyperpage}{8
2
}
\indexentry{树库|hyperpage}{8
1
}
\indexentry{Treebank|hyperpage}{8
2
}
\indexentry{Treebank|hyperpage}{8
1
}
\indexentry{生成模型|hyperpage}{83}
\indexentry{生成模型|hyperpage}{83}
\indexentry{Generative Model|hyperpage}{83}
\indexentry{Generative Model|hyperpage}{83}
\indexentry{判别模型|hyperpage}{83}
\indexentry{判别模型|hyperpage}{83}
...
@@ -153,525 +153,525 @@
...
@@ -153,525 +153,525 @@
\indexentry{The Lagrange Multiplier Method|hyperpage}{113}
\indexentry{The Lagrange Multiplier Method|hyperpage}{113}
\indexentry{期望最大化|hyperpage}{115}
\indexentry{期望最大化|hyperpage}{115}
\indexentry{Expectation Maximization|hyperpage}{115}
\indexentry{Expectation Maximization|hyperpage}{115}
\indexentry{期望频次|hyperpage}{11
6
}
\indexentry{期望频次|hyperpage}{11
5
}
\indexentry{Expected Count|hyperpage}{11
6
}
\indexentry{Expected Count|hyperpage}{11
5
}
\indexentry{产出率|hyperpage}{11
9
}
\indexentry{产出率|hyperpage}{11
8
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\indexentry{繁衍率|hyperpage}{11
9
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\indexentry{繁衍率|hyperpage}{11
8
}
\indexentry{Fertility|hyperpage}{11
9
}
\indexentry{Fertility|hyperpage}{11
8
}
\indexentry{扭曲度|hyperpage}{12
1
}
\indexentry{扭曲度|hyperpage}{12
0
}
\indexentry{Distortion|hyperpage}{12
1
}
\indexentry{Distortion|hyperpage}{12
0
}
\indexentry{概念单元|hyperpage}{12
3
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\indexentry{概念单元|hyperpage}{12
2
}
\indexentry{概念|hyperpage}{12
3
}
\indexentry{概念|hyperpage}{12
2
}
\indexentry{Concept|hyperpage}{12
3
}
\indexentry{Concept|hyperpage}{12
2
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\indexentry{缺陷|hyperpage}{12
5
}
\indexentry{缺陷|hyperpage}{12
4
}
\indexentry{Deficiency|hyperpage}{12
5
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\indexentry{Deficiency|hyperpage}{12
4
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\indexentry{凸函数|hyperpage}{12
9
}
\indexentry{凸函数|hyperpage}{12
8
}
\indexentry{Convex function|hyperpage}{12
9
}
\indexentry{Convex function|hyperpage}{12
8
}
\indexentry{对称化|hyperpage}{1
30
}
\indexentry{对称化|hyperpage}{1
29
}
\indexentry{Symmetrization|hyperpage}{1
30
}
\indexentry{Symmetrization|hyperpage}{1
29
}
\indexentry{系统偏置|hyperpage}{13
1
}
\indexentry{系统偏置|hyperpage}{13
0
}
\indexentry{System Bias|hyperpage}{13
1
}
\indexentry{System Bias|hyperpage}{13
0
}
\indexentry{组合性翻译|hyperpage}{13
6
}
\indexentry{组合性翻译|hyperpage}{13
4
}
\indexentry{Compositional Translation|hyperpage}{13
6
}
\indexentry{Compositional Translation|hyperpage}{13
4
}
\indexentry{短语|hyperpage}{13
6
}
\indexentry{短语|hyperpage}{13
4
}
\indexentry{短语切分|hyperpage}{1
41
}
\indexentry{短语切分|hyperpage}{1
39
}
\indexentry{Phrasal Segmentation|hyperpage}{1
41
}
\indexentry{Phrasal Segmentation|hyperpage}{1
39
}
\indexentry{短语对|hyperpage}{1
41
}
\indexentry{短语对|hyperpage}{1
39
}
\indexentry{推导|hyperpage}{14
1
}
\indexentry{推导|hyperpage}{14
0
}
\indexentry{Derivation|hyperpage}{14
1
}
\indexentry{Derivation|hyperpage}{14
0
}
\indexentry{生成式模型|hyperpage}{14
4
}
\indexentry{生成式模型|hyperpage}{14
2
}
\indexentry{Generative Model|hyperpage}{14
4
}
\indexentry{Generative Model|hyperpage}{14
2
}
\indexentry{判别式模型|hyperpage}{14
4
}
\indexentry{判别式模型|hyperpage}{14
3
}
\indexentry{Discriminative Model|hyperpage}{14
4
}
\indexentry{Discriminative Model|hyperpage}{14
3
}
\indexentry{对数线性模型|hyperpage}{14
5
}
\indexentry{对数线性模型|hyperpage}{14
3
}
\indexentry{Log-linear Model|hyperpage}{14
5
}
\indexentry{Log-linear Model|hyperpage}{14
3
}
\indexentry{短语抽取|hyperpage}{14
6
}
\indexentry{短语抽取|hyperpage}{14
4
}
\indexentry{Phrase Extraction|hyperpage}{14
6
}
\indexentry{Phrase Extraction|hyperpage}{14
4
}
\indexentry{词汇化翻译概率|hyperpage}{14
9
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7
}
\indexentry{Lexical Translation Probability|hyperpage}{14
9
}
\indexentry{Lexical Translation Probability|hyperpage}{14
7
}
\indexentry{短语表|hyperpage}{1
50
}
\indexentry{短语表|hyperpage}{1
48
}
\indexentry{Phrase Table|hyperpage}{1
50
}
\indexentry{Phrase Table|hyperpage}{1
48
}
\indexentry{调序|hyperpage}{1
50
}
\indexentry{调序|hyperpage}{1
48
}
\indexentry{Reordering|hyperpage}{1
50
}
\indexentry{Reordering|hyperpage}{1
48
}
\indexentry{模型训练|hyperpage}{15
4
}
\indexentry{模型训练|hyperpage}{15
2
}
\indexentry{Model Training|hyperpage}{15
4
}
\indexentry{Model Training|hyperpage}{15
2
}
\indexentry{权重调优|hyperpage}{15
4
}
\indexentry{权重调优|hyperpage}{15
2
}
\indexentry{Weight Tuning|hyperpage}{15
4
}
\indexentry{Weight Tuning|hyperpage}{15
2
}
\indexentry{最小错误率训练|hyperpage}{15
4
}
\indexentry{最小错误率训练|hyperpage}{15
2
}
\indexentry{Minimum Error Rate Training|hyperpage}{15
4
}
\indexentry{Minimum Error Rate Training|hyperpage}{15
2
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\indexentry{调优集合|hyperpage}{15
4
}
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2
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\indexentry{Tuning Set|hyperpage}{15
4
}
\indexentry{Tuning Set|hyperpage}{15
2
}
\indexentry{线搜索|hyperpage}{15
5
}
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3
}
\indexentry{Line Search|hyperpage}{15
5
}
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3
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6
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4
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6
}
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4
}
\indexentry{覆盖度模型|hyperpage}{15
8
}
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6
}
\indexentry{Coverage Model|hyperpage}{15
8
}
\indexentry{Coverage Model|hyperpage}{15
6
}
\indexentry{翻译候选|hyperpage}{15
8
}
\indexentry{翻译候选|hyperpage}{15
6
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\indexentry{Translation Candidate|hyperpage}{15
8
}
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6
}
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9
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7
}
\indexentry{Translation Hypothesis|hyperpage}{15
9
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\indexentry{Translation Hypothesis|hyperpage}{15
7
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\indexentry{剪枝|hyperpage}{1
60
}
\indexentry{剪枝|hyperpage}{1
58
}
\indexentry{Pruning|hyperpage}{1
60
}
\indexentry{Pruning|hyperpage}{1
58
}
\indexentry{束剪枝|hyperpage}{1
60
}
\indexentry{束剪枝|hyperpage}{1
58
}
\indexentry{Beam Pruning|hyperpage}{1
60
}
\indexentry{Beam Pruning|hyperpage}{1
58
}
\indexentry{直方图剪枝|hyperpage}{1
60
}
\indexentry{直方图剪枝|hyperpage}{1
58
}
\indexentry{Histogram Pruning|hyperpage}{1
60
}
\indexentry{Histogram Pruning|hyperpage}{1
58
}
\indexentry{阈值剪枝|hyperpage}{1
60
}
\indexentry{阈值剪枝|hyperpage}{1
58
}
\indexentry{Threshold Pruning|hyperpage}{1
60
}
\indexentry{Threshold Pruning|hyperpage}{1
58
}
\indexentry{假设重组|hyperpage}{1
60
}
\indexentry{假设重组|hyperpage}{1
58
}
\indexentry{Hypothesis Recombination|hyperpage}{1
60
}
\indexentry{Hypothesis Recombination|hyperpage}{1
58
}
\indexentry{基于层次短语的模型|hyperpage}{16
4
}
\indexentry{基于层次短语的模型|hyperpage}{16
3
}
\indexentry{Hierarchical Phrase-based Model|hyperpage}{16
4
}
\indexentry{Hierarchical Phrase-based Model|hyperpage}{16
3
}
\indexentry{同步上下文无关文法|hyperpage}{16
5
}
\indexentry{同步上下文无关文法|hyperpage}{16
3
}
\indexentry{Synchronous Context-free Grammar|hyperpage}{16
5
}
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3
}
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6
}
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4
}
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6
}
\indexentry{Hierarchical Phrase-based Grammar|hyperpage}{16
4
}
\indexentry{推导|hyperpage}{16
7
}
\indexentry{推导|hyperpage}{16
5
}
\indexentry{Derivation|hyperpage}{16
7
}
\indexentry{Derivation|hyperpage}{16
5
}
\indexentry{胶水规则|hyperpage}{16
7
}
\indexentry{胶水规则|hyperpage}{16
5
}
\indexentry{Glue Rule|hyperpage}{16
7
}
\indexentry{Glue Rule|hyperpage}{16
5
}
\indexentry{乔姆斯基范式|hyperpage}{1
71
}
\indexentry{乔姆斯基范式|hyperpage}{1
69
}
\indexentry{Chomsky Normal Form|hyperpage}{1
71
}
\indexentry{Chomsky Normal Form|hyperpage}{1
69
}
\indexentry{跨度|hyperpage}{1
71
}
\indexentry{跨度|hyperpage}{1
69
}
\indexentry{Span|hyperpage}{1
71
}
\indexentry{Span|hyperpage}{1
69
}
\indexentry{自下而上的分析|hyperpage}{17
2
}
\indexentry{自下而上的分析|hyperpage}{17
0
}
\indexentry{Top-down Parsing|hyperpage}{17
2
}
\indexentry{Top-down Parsing|hyperpage}{17
0
}
\indexentry{束剪枝|hyperpage}{17
4
}
\indexentry{束剪枝|hyperpage}{17
2
}
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4
}
\indexentry{Beam Pruning|hyperpage}{17
2
}
\indexentry{立方剪枝|hyperpage}{17
6
}
\indexentry{立方剪枝|hyperpage}{17
4
}
\indexentry{Cube Pruning|hyperpage}{17
6
}
\indexentry{Cube Pruning|hyperpage}{17
4
}
\indexentry{序列化|hyperpage}{179}
\indexentry{序列化|hyperpage}{179}
\indexentry{线性化|hyperpage}{179}
\indexentry{线性化|hyperpage}{179}
\indexentry{Linearization|hyperpage}{179}
\indexentry{Linearization|hyperpage}{179}
\indexentry{树到串翻译规则|hyperpage}{1
81
}
\indexentry{树到串翻译规则|hyperpage}{1
79
}
\indexentry{Tree-to-String Translation Rule|hyperpage}{1
81
}
\indexentry{Tree-to-String Translation Rule|hyperpage}{1
79
}
\indexentry{树到树翻译规则|hyperpage}{1
81
}
\indexentry{树到树翻译规则|hyperpage}{1
79
}
\indexentry{Tree-to-Tree Translation Rule|hyperpage}{1
81
}
\indexentry{Tree-to-Tree Translation Rule|hyperpage}{1
79
}
\indexentry{树片段|hyperpage}{18
2
}
\indexentry{树片段|hyperpage}{18
0
}
\indexentry{Tree Fragment|hyperpage}{18
2
}
\indexentry{Tree Fragment|hyperpage}{18
0
}
\indexentry{同步树替换文法规则|hyperpage}{18
3
}
\indexentry{同步树替换文法规则|hyperpage}{18
1
}
\indexentry{Synchronous Tree Substitution Grammar Rule|hyperpage}{18
3
}
\indexentry{Synchronous Tree Substitution Grammar Rule|hyperpage}{18
1
}
\indexentry{边缘集合|hyperpage}{18
9
}
\indexentry{边缘集合|hyperpage}{18
7
}
\indexentry{Frontier Set|hyperpage}{18
9
}
\indexentry{Frontier Set|hyperpage}{18
7
}
\indexentry{最小规则|hyperpage}{1
90
}
\indexentry{最小规则|hyperpage}{1
88
}
\indexentry{Minimal Rules|hyperpage}{1
90
}
\indexentry{Minimal Rules|hyperpage}{1
88
}
\indexentry{二叉化|hyperpage}{19
4
}
\indexentry{二叉化|hyperpage}{19
1
}
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4
}
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1
}
\indexentry{基于短语的特征|hyperpage}{19
8
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5
}
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8
}
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5
}
\indexentry{有向超图|hyperpage}{19
9
}
\indexentry{有向超图|hyperpage}{19
6
}
\indexentry{Directed Hyper-graph|hyperpage}{19
9
}
\indexentry{Directed Hyper-graph|hyperpage}{19
6
}
\indexentry{超边|hyperpage}{19
9
}
\indexentry{超边|hyperpage}{19
6
}
\indexentry{Hyper-edge|hyperpage}{19
9
}
\indexentry{Hyper-edge|hyperpage}{19
6
}
\indexentry{半环分析|hyperpage}{
200
}
\indexentry{半环分析|hyperpage}{
197
}
\indexentry{Semi-ring Parsing|hyperpage}{
200
}
\indexentry{Semi-ring Parsing|hyperpage}{
197
}
\indexentry{组合|hyperpage}{
201
}
\indexentry{组合|hyperpage}{
198
}
\indexentry{Composition|hyperpage}{
201
}
\indexentry{Composition|hyperpage}{
198
}
\indexentry{基于串的解码|hyperpage}{
201
}
\indexentry{基于串的解码|hyperpage}{
199
}
\indexentry{String-based Decoding|hyperpage}{
201
}
\indexentry{String-based Decoding|hyperpage}{
199
}
\indexentry{基于树的解码|hyperpage}{
201
}
\indexentry{基于树的解码|hyperpage}{
199
}
\indexentry{Tree-based Decoding|hyperpage}{
201
}
\indexentry{Tree-based Decoding|hyperpage}{
199
}
\indexentry{Lexicalized Norm Form|hyperpage}{20
5
}
\indexentry{Lexicalized Norm Form|hyperpage}{20
2
}
\indexentry{人工神经网络|hyperpage}{2
11
}
\indexentry{人工神经网络|hyperpage}{2
07
}
\indexentry{Artificial Neural Networks|hyperpage}{2
11
}
\indexentry{Artificial Neural Networks|hyperpage}{2
07
}
\indexentry{神经网络|hyperpage}{2
11
}
\indexentry{神经网络|hyperpage}{2
07
}
\indexentry{Neural Networks|hyperpage}{2
11
}
\indexentry{Neural Networks|hyperpage}{2
07
}
\indexentry{深度学习|hyperpage}{2
12
}
\indexentry{深度学习|hyperpage}{2
08
}
\indexentry{Deep Learning|hyperpage}{2
12
}
\indexentry{Deep Learning|hyperpage}{2
08
}
\indexentry{连接主义|hyperpage}{2
13
}
\indexentry{连接主义|hyperpage}{2
09
}
\indexentry{Connectionism|hyperpage}{2
13
}
\indexentry{Connectionism|hyperpage}{2
09
}
\indexentry{分布式表示|hyperpage}{2
13
}
\indexentry{分布式表示|hyperpage}{2
09
}
\indexentry{Distributed representation|hyperpage}{2
13
}
\indexentry{Distributed representation|hyperpage}{2
09
}
\indexentry{符号主义|hyperpage}{2
13
}
\indexentry{符号主义|hyperpage}{2
09
}
\indexentry{Symbolicism|hyperpage}{2
13
}
\indexentry{Symbolicism|hyperpage}{2
09
}
\indexentry{端到端学习|hyperpage}{21
5
}
\indexentry{端到端学习|hyperpage}{21
1
}
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5
}
\indexentry{End-to-End Learning|hyperpage}{21
1
}
\indexentry{表示学习|hyperpage}{21
5
}
\indexentry{表示学习|hyperpage}{21
1
}
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5
}
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1
}
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6
}
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2
}
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6
}
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2
}
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7
}
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3
}
\indexentry{Scalar|hyperpage}{21
7
}
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3
}
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7
}
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3
}
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3
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7
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3
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7
}
\indexentry{Matrix|hyperpage}{21
3
}
\indexentry{转置|hyperpage}{21
8
}
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4
}
\indexentry{Transpose|hyperpage}{21
8
}
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4
}
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8
}
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4
}
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8
}
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4
}
\indexentry{数乘|hyperpage}{21
9
}
\indexentry{数乘|hyperpage}{21
5
}
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9
}
\indexentry{Scalar Multiplication|hyperpage}{21
5
}
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20
}
\indexentry{按元素乘积|hyperpage}{2
16
}
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20
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16
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20
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16
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20
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16
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20
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16
}
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20
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16
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21
}
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17
}
\indexentry{Norm|hyperpage}{2
21
}
\indexentry{Norm|hyperpage}{2
17
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22
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18
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22
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18
}
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22
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18
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22
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18
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23
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\indexentry{权重|hyperpage}{2
19
}
\indexentry{weight|hyperpage}{2
23
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19
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33
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29
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\indexentry{Tensor|hyperpage}{2
33
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29
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33
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\indexentry{阶|hyperpage}{2
29
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\indexentry{Rank|hyperpage}{2
33
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29
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7
}
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3
}
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7
}
\indexentry{向量化|hyperpage}{23
3
}
\indexentry{Vectorization|hyperpage}{23
7
}
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3
}
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41
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\indexentry{前向传播|hyperpage}{2
37
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\indexentry{计算图|hyperpage}{2
42
}
\indexentry{计算图|hyperpage}{2
38
}
\indexentry{Computation Graph|hyperpage}{2
42
}
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38
}
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43
}
\indexentry{模型参数|hyperpage}{2
39
}
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43
}
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39
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43
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39
}
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43
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39
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43
}
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39
}
\indexentry{Annotated Data/Labeled Data|hyperpage}{2
43
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39
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39
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43
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39
}
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43
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39
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4
}
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0
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4
}
\indexentry{Training Data Set|hyperpage}{24
0
}
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4
}
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0
}
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4
}
\indexentry{Loss Function|hyperpage}{24
0
}
\indexentry{目标函数|hyperpage}{24
4
}
\indexentry{目标函数|hyperpage}{24
0
}
\indexentry{Objective Function|hyperpage}{24
4
}
\indexentry{Objective Function|hyperpage}{24
0
}
\indexentry{代价函数|hyperpage}{24
6
}
\indexentry{代价函数|hyperpage}{24
2
}
\indexentry{Cost Function|hyperpage}{24
6
}
\indexentry{Cost Function|hyperpage}{24
2
}
\indexentry{梯度下降方法|hyperpage}{24
6
}
\indexentry{梯度下降方法|hyperpage}{24
2
}
\indexentry{Gradient Descent Method|hyperpage}{24
6
}
\indexentry{Gradient Descent Method|hyperpage}{24
2
}
\indexentry{参数更新的规则|hyperpage}{24
6
}
\indexentry{参数更新的规则|hyperpage}{24
2
}
\indexentry{Update Rule|hyperpage}{24
6
}
\indexentry{Update Rule|hyperpage}{24
2
}
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6
}
\indexentry{学习率|hyperpage}{24
2
}
\indexentry{Learning Rate|hyperpage}{24
6
}
\indexentry{Learning Rate|hyperpage}{24
2
}
\indexentry{基于梯度的方法|hyperpage}{24
6
}
\indexentry{基于梯度的方法|hyperpage}{24
2
}
\indexentry{Gradient-based Method|hyperpage}{24
6
}
\indexentry{Gradient-based Method|hyperpage}{24
2
}
\indexentry{批量梯度下降|hyperpage}{24
7
}
\indexentry{批量梯度下降|hyperpage}{24
3
}
\indexentry{Batch Gradient Descent|hyperpage}{24
7
}
\indexentry{Batch Gradient Descent|hyperpage}{24
3
}
\indexentry{随机梯度下降|hyperpage}{24
7
}
\indexentry{随机梯度下降|hyperpage}{24
3
}
\indexentry{Stochastic Gradient Descent|hyperpage}{24
7
}
\indexentry{Stochastic Gradient Descent|hyperpage}{24
3
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7
}
\indexentry{小批量梯度下降|hyperpage}{24
3
}
\indexentry{Mini-Batch Gradient Descent|hyperpage}{24
7
}
\indexentry{Mini-Batch Gradient Descent|hyperpage}{24
3
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\indexentry{数值微分|hyperpage}{24
8
}
\indexentry{数值微分|hyperpage}{24
4
}
\indexentry{Numerical Differentiation|hyperpage}{24
8
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\indexentry{Numerical Differentiation|hyperpage}{24
4
}
\indexentry{截断误差|hyperpage}{24
8
}
\indexentry{截断误差|hyperpage}{24
4
}
\indexentry{Truncation Error|hyperpage}{24
8
}
\indexentry{Truncation Error|hyperpage}{24
4
}
\indexentry{舍入误差|hyperpage}{24
8
}
\indexentry{舍入误差|hyperpage}{24
4
}
\indexentry{Round-off Error|hyperpage}{24
8
}
\indexentry{Round-off Error|hyperpage}{24
4
}
\indexentry{符号微分|hyperpage}{24
9
}
\indexentry{符号微分|hyperpage}{24
5
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\indexentry{Symbolic Differentiation|hyperpage}{24
9
}
\indexentry{Symbolic Differentiation|hyperpage}{24
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\indexentry{表达式膨胀|hyperpage}{24
9
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\indexentry{表达式膨胀|hyperpage}{24
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\indexentry{Expression Swell|hyperpage}{24
9
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\indexentry{Expression Swell|hyperpage}{24
5
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\indexentry{自动微分|hyperpage}{24
9
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\indexentry{自动微分|hyperpage}{24
5
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\indexentry{Automatic Differentiation|hyperpage}{24
9
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\indexentry{Automatic Differentiation|hyperpage}{24
5
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\indexentry{反向模式|hyperpage}{2
50
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\indexentry{反向模式|hyperpage}{2
46
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\indexentry{Backward Mode|hyperpage}{2
50
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\indexentry{Backward Mode|hyperpage}{2
46
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51
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47
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\indexentry{Learning Rate|hyperpage}{2
51
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\indexentry{Learning Rate|hyperpage}{2
47
}
\indexentry{Momentum|hyperpage}{2
51
}
\indexentry{Momentum|hyperpage}{2
47
}
\indexentry{AdaGrad|hyperpage}{2
52
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\indexentry{AdaGrad|hyperpage}{2
48
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\indexentry{衰减|hyperpage}{2
52
}
\indexentry{衰减|hyperpage}{2
48
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\indexentry{Decay|hyperpage}{2
52
}
\indexentry{Decay|hyperpage}{2
48
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\indexentry{RMSprop|hyperpage}{2
52
}
\indexentry{RMSprop|hyperpage}{2
48
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\indexentry{Adam|hyperpage}{2
53
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\indexentry{Adam|hyperpage}{2
49
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53
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49
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\indexentry{同步更新|hyperpage}{25
4
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\indexentry{同步更新|hyperpage}{25
0
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\indexentry{Synchronous Update|hyperpage}{25
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\indexentry{Synchronous Update|hyperpage}{25
0
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\indexentry{异步更新|hyperpage}{25
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\indexentry{异步更新|hyperpage}{25
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\indexentry{Asynchronous Update|hyperpage}{25
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\indexentry{Asynchronous Update|hyperpage}{25
0
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\indexentry{参数服务器|hyperpage}{25
4
}
\indexentry{参数服务器|hyperpage}{25
0
}
\indexentry{Parameter Server|hyperpage}{25
4
}
\indexentry{Parameter Server|hyperpage}{25
0
}
\indexentry{梯度消失|hyperpage}{25
5
}
\indexentry{梯度消失|hyperpage}{25
1
}
\indexentry{Gradient Vanishing|hyperpage}{25
5
}
\indexentry{Gradient Vanishing|hyperpage}{25
1
}
\indexentry{梯度爆炸|hyperpage}{25
5
}
\indexentry{梯度爆炸|hyperpage}{25
1
}
\indexentry{Gradient Explosion|hyperpage}{25
5
}
\indexentry{Gradient Explosion|hyperpage}{25
1
}
\indexentry{梯度裁剪|hyperpage}{25
6
}
\indexentry{梯度裁剪|hyperpage}{25
2
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\indexentry{Gradient Clipping|hyperpage}{25
6
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\indexentry{Gradient Clipping|hyperpage}{25
2
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7
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\indexentry{批量归一化|hyperpage}{25
3
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\indexentry{Batch Normalization|hyperpage}{25
7
}
\indexentry{Batch Normalization|hyperpage}{25
3
}
\indexentry{层归一化|hyperpage}{25
7
}
\indexentry{层归一化|hyperpage}{25
3
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\indexentry{Layer Normalization|hyperpage}{25
7
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\indexentry{Layer Normalization|hyperpage}{25
3
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\indexentry{残差网络|hyperpage}{25
7
}
\indexentry{残差网络|hyperpage}{25
3
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\indexentry{Residual Networks|hyperpage}{25
7
}
\indexentry{Residual Networks|hyperpage}{25
3
}
\indexentry{跳接|hyperpage}{25
7
}
\indexentry{跳接|hyperpage}{25
3
}
\indexentry{Shortcut Connection|hyperpage}{25
7
}
\indexentry{Shortcut Connection|hyperpage}{25
3
}
\indexentry{过拟合|hyperpage}{25
8
}
\indexentry{过拟合|hyperpage}{25
4
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\indexentry{Overfitting|hyperpage}{25
8
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\indexentry{Overfitting|hyperpage}{25
4
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\indexentry{正则化|hyperpage}{25
8
}
\indexentry{正则化|hyperpage}{25
4
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\indexentry{Regularization|hyperpage}{25
8
}
\indexentry{Regularization|hyperpage}{25
4
}
\indexentry{反向传播|hyperpage}{25
9
}
\indexentry{反向传播|hyperpage}{25
5
}
\indexentry{back propagation|hyperpage}{25
9
}
\indexentry{back propagation|hyperpage}{25
5
}
\indexentry{神经语言模型|hyperpage}{26
5
}
\indexentry{神经语言模型|hyperpage}{26
1
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\indexentry{Neural Language Model|hyperpage}{26
5
}
\indexentry{Neural Language Model|hyperpage}{26
1
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\indexentry{前馈神经网络语言模型|hyperpage}{26
6
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2
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6
}
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2
}
\indexentry{循环神经网络|hyperpage}{26
8
}
\indexentry{循环神经网络|hyperpage}{26
4
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8
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\indexentry{Recurrent Neural Network|hyperpage}{26
4
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8
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4
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8
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\indexentry{RNNLM|hyperpage}{26
4
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8
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\indexentry{循环单元|hyperpage}{26
4
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\indexentry{RNN Cell|hyperpage}{26
8
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\indexentry{RNN Cell|hyperpage}{26
4
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\indexentry{自注意力机制|hyperpage}{2
70
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\indexentry{自注意力机制|hyperpage}{2
66
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\indexentry{Self-Attention Mechanism|hyperpage}{2
70
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\indexentry{Self-Attention Mechanism|hyperpage}{2
66
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\indexentry{注意力权重|hyperpage}{2
70
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\indexentry{注意力权重|hyperpage}{2
66
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\indexentry{Attention Weight|hyperpage}{2
70
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\indexentry{Attention Weight|hyperpage}{2
66
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\indexentry{困惑度|hyperpage}{2
71
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\indexentry{困惑度|hyperpage}{2
67
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\indexentry{Perplexity|hyperpage}{2
71
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\indexentry{Perplexity|hyperpage}{2
67
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\indexentry{One-hot编码|hyperpage}{2
71
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\indexentry{One-hot编码|hyperpage}{2
67
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\indexentry{独热编码|hyperpage}{2
71
}
\indexentry{独热编码|hyperpage}{2
67
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\indexentry{分布式表示|hyperpage}{2
72
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\indexentry{分布式表示|hyperpage}{2
68
}
\indexentry{Distributed Representation|hyperpage}{2
72
}
\indexentry{Distributed Representation|hyperpage}{2
68
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\indexentry{词嵌入|hyperpage}{2
72
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\indexentry{词嵌入|hyperpage}{2
68
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\indexentry{Word Embedding|hyperpage}{2
72
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\indexentry{Word Embedding|hyperpage}{2
68
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\indexentry{句子表示模型|hyperpage}{27
4
}
\indexentry{句子表示模型|hyperpage}{27
0
}
\indexentry{句子的表示|hyperpage}{27
4
}
\indexentry{句子的表示|hyperpage}{27
0
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4
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\indexentry{表示学习|hyperpage}{27
0
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4
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0
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8
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\indexentry{可解释机器学习|hyperpage}{27
4
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\indexentry{Explainable Machine Learning|hyperpage}{27
8
}
\indexentry{Explainable Machine Learning|hyperpage}{27
4
}
\indexentry{神经机器翻译|hyperpage}{2
81
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\indexentry{神经机器翻译|hyperpage}{2
75
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\indexentry{Neural Machine Translation|hyperpage}{2
81
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\indexentry{Neural Machine Translation|hyperpage}{2
75
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\indexentry{分布式表示|hyperpage}{2
83
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\indexentry{分布式表示|hyperpage}{2
77
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\indexentry{Distributed Representation|hyperpage}{2
83
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\indexentry{Distributed Representation|hyperpage}{2
77
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\indexentry{特征工程|hyperpage}{28
9
}
\indexentry{特征工程|hyperpage}{28
3
}
\indexentry{Feature Engineering|hyperpage}{28
9
}
\indexentry{Feature Engineering|hyperpage}{28
3
}
\indexentry{编码器-解码器模型|hyperpage}{2
90
}
\indexentry{编码器-解码器模型|hyperpage}{2
83
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\indexentry{Encoder-Decoder Paradigm|hyperpage}{2
90
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\indexentry{Encoder-Decoder Paradigm|hyperpage}{2
83
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\indexentry{编码器-解码器框架|hyperpage}{2
90
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\indexentry{编码器-解码器框架|hyperpage}{2
83
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\indexentry{循环神经网络|hyperpage}{2
95
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\indexentry{循环神经网络|hyperpage}{2
89
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95
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\indexentry{Recurrent Neural Network, RNN|hyperpage}{2
89
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\indexentry{词嵌入|hyperpage}{29
7
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\indexentry{词嵌入|hyperpage}{29
1
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\indexentry{Word Embedding|hyperpage}{29
7
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\indexentry{Word Embedding|hyperpage}{29
1
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1
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7
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\indexentry{Representation Learning|hyperpage}{29
1
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\indexentry{生成|hyperpage}{29
7
}
\indexentry{生成|hyperpage}{29
1
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\indexentry{Generation|hyperpage}{29
7
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\indexentry{Generation|hyperpage}{29
1
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\indexentry{长短时记忆|hyperpage}{
302
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\indexentry{长短时记忆|hyperpage}{
295
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\indexentry{Long Short-Term Memory|hyperpage}{
302
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\indexentry{Long Short-Term Memory|hyperpage}{
295
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\indexentry{遗忘|hyperpage}{
302
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\indexentry{遗忘|hyperpage}{
296
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\indexentry{记忆更新|hyperpage}{
303
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\indexentry{记忆更新|hyperpage}{
296
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\indexentry{输出|hyperpage}{
303
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\indexentry{输出|hyperpage}{
297
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\indexentry{门循环单元|hyperpage}{
304
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\indexentry{门循环单元|hyperpage}{
298
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\indexentry{Gated Recurrent Unit，GRU|hyperpage}{
304
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\indexentry{Gated Recurrent Unit，GRU|hyperpage}{
298
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\indexentry{注意力权重|hyperpage}{30
9
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\indexentry{注意力权重|hyperpage}{30
3
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\indexentry{Attention Weight|hyperpage}{30
9
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\indexentry{Attention Weight|hyperpage}{30
3
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\indexentry{一阶矩估计|hyperpage}{3
15
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\indexentry{一阶矩估计|hyperpage}{3
09
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\indexentry{First Moment Estimation|hyperpage}{3
15
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\indexentry{First Moment Estimation|hyperpage}{3
09
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\indexentry{二阶矩估计|hyperpage}{3
15
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\indexentry{二阶矩估计|hyperpage}{3
09
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\indexentry{Second Moment Estimation|hyperpage}{3
15
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\indexentry{Second Moment Estimation|hyperpage}{3
09
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\indexentry{学习率|hyperpage}{3
16
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\indexentry{学习率|hyperpage}{3
09
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\indexentry{Learning Rate|hyperpage}{3
16
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\indexentry{Learning Rate|hyperpage}{3
09
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\indexentry{逐渐预热|hyperpage}{31
6
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\indexentry{逐渐预热|hyperpage}{31
0
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\indexentry{Gradual Warmup|hyperpage}{31
6
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\indexentry{Gradual Warmup|hyperpage}{31
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\indexentry{分段常数衰减|hyperpage}{31
7
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\indexentry{分段常数衰减|hyperpage}{31
1
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\indexentry{Piecewise Constant Decay|hyperpage}{31
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\indexentry{Piecewise Constant Decay|hyperpage}{31
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\indexentry{数据并行|hyperpage}{31
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\indexentry{模型并行|hyperpage}{31
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\indexentry{模型并行|hyperpage}{31
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\indexentry{全搜索|hyperpage}{3
20
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\indexentry{全搜索|hyperpage}{3
13
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\indexentry{Full Search|hyperpage}{3
20
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\indexentry{Full Search|hyperpage}{3
13
}
\indexentry{贪婪搜索|hyperpage}{3
20
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\indexentry{贪婪搜索|hyperpage}{3
13
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\indexentry{Greedy Search|hyperpage}{3
20
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\indexentry{Greedy Search|hyperpage}{3
13
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\indexentry{束搜索|hyperpage}{3
20
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\indexentry{束搜索|hyperpage}{3
14
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\indexentry{Beam Search|hyperpage}{3
20
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\indexentry{Beam Search|hyperpage}{3
14
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20
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\indexentry{自回归模型|hyperpage}{3
14
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\indexentry{Autoregressive Model|hyperpage}{3
21
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\indexentry{Autoregressive Model|hyperpage}{3
14
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\indexentry{非自回归模型|hyperpage}{3
21
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\indexentry{非自回归模型|hyperpage}{3
14
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21
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\indexentry{Non-autoregressive Model|hyperpage}{3
14
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6
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\indexentry{自注意力机制|hyperpage}{32
0
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\indexentry{Self-Attention|hyperpage}{32
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8
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\indexentry{自注意力子层|hyperpage}{32
1
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}
\indexentry{前馈神经网络子层|hyperpage}{32
8
}
\indexentry{前馈神经网络子层|hyperpage}{32
2
}
\indexentry{Feed-forward Sub-layer|hyperpage}{32
8
}
\indexentry{Feed-forward Sub-layer|hyperpage}{32
2
}
\indexentry{残差连接|hyperpage}{32
8
}
\indexentry{残差连接|hyperpage}{32
2
}
\indexentry{Residual Connection|hyperpage}{32
8
}
\indexentry{Residual Connection|hyperpage}{32
2
}
\indexentry{层正则化|hyperpage}{32
8
}
\indexentry{层正则化|hyperpage}{32
2
}
\indexentry{Layer Normalization|hyperpage}{32
8
}
\indexentry{Layer Normalization|hyperpage}{32
2
}
\indexentry{编码-解码注意力子层|hyperpage}{32
9
}
\indexentry{编码-解码注意力子层|hyperpage}{32
2
}
\indexentry{Encoder-decoder Attention Sub-layer|hyperpage}{32
9
}
\indexentry{Encoder-decoder Attention Sub-layer|hyperpage}{32
2
}
\indexentry{词嵌入|hyperpage}{32
9
}
\indexentry{词嵌入|hyperpage}{32
3
}
\indexentry{Word Embedding|hyperpage}{32
9
}
\indexentry{Word Embedding|hyperpage}{32
3
}
\indexentry{位置编码|hyperpage}{32
9
}
\indexentry{位置编码|hyperpage}{32
3
}
\indexentry{Position Embedding|hyperpage}{32
9
}
\indexentry{Position Embedding|hyperpage}{32
3
}
\indexentry{点乘注意力|hyperpage}{3
33
}
\indexentry{点乘注意力|hyperpage}{3
26
}
\indexentry{Scaled Dot-Product Attention|hyperpage}{3
33
}
\indexentry{Scaled Dot-Product Attention|hyperpage}{3
26
}
\indexentry{多头注意力|hyperpage}{3
35
}
\indexentry{多头注意力|hyperpage}{3
28
}
\indexentry{Multi-head Attention|hyperpage}{3
35
}
\indexentry{Multi-head Attention|hyperpage}{3
28
}
\indexentry{残差连接|hyperpage}{3
36
}
\indexentry{残差连接|hyperpage}{3
29
}
\indexentry{短连接|hyperpage}{33
7
}
\indexentry{短连接|hyperpage}{33
0
}
\indexentry{Short-cut Connection|hyperpage}{33
7
}
\indexentry{Short-cut Connection|hyperpage}{33
0
}
\indexentry{后正则化|hyperpage}{33
8
}
\indexentry{后正则化|hyperpage}{33
1
}
\indexentry{Post-norm|hyperpage}{33
8
}
\indexentry{Post-norm|hyperpage}{33
1
}
\indexentry{前正则化|hyperpage}{33
8
}
\indexentry{前正则化|hyperpage}{33
1
}
\indexentry{Pre-norm|hyperpage}{33
8
}
\indexentry{Pre-norm|hyperpage}{33
1
}
\indexentry{交叉熵损失|hyperpage}{33
9
}
\indexentry{交叉熵损失|hyperpage}{33
2
}
\indexentry{Cross Entropy Loss|hyperpage}{33
9
}
\indexentry{Cross Entropy Loss|hyperpage}{33
2
}
\indexentry{预热|hyperpage}{33
9
}
\indexentry{预热|hyperpage}{33
2
}
\indexentry{Warmup|hyperpage}{33
9
}
\indexentry{Warmup|hyperpage}{33
2
}
\indexentry{小批量训练|hyperpage}{3
40
}
\indexentry{小批量训练|hyperpage}{3
33
}
\indexentry{Mini-batch Training|hyperpage}{3
40
}
\indexentry{Mini-batch Training|hyperpage}{3
33
}
\indexentry{Dropout|hyperpage}{3
40
}
\indexentry{Dropout|hyperpage}{3
33
}
\indexentry{过拟合|hyperpage}{3
40
}
\indexentry{过拟合|hyperpage}{3
33
}
\indexentry{Over fitting|hyperpage}{3
40
}
\indexentry{Over fitting|hyperpage}{3
33
}
\indexentry{标签平滑|hyperpage}{3
40
}
\indexentry{标签平滑|hyperpage}{3
33
}
\indexentry{Label Smoothing|hyperpage}{3
40
}
\indexentry{Label Smoothing|hyperpage}{3
33
}
\indexentry{序列到序列的转换/生成问题|hyperpage}{3
42
}
\indexentry{序列到序列的转换/生成问题|hyperpage}{3
35
}
\indexentry{Sequence-to-Sequence Problem|hyperpage}{3
42
}
\indexentry{Sequence-to-Sequence Problem|hyperpage}{3
35
}
\indexentry{未登录词|hyperpage}{3
53
}
\indexentry{未登录词|hyperpage}{3
45
}
\indexentry{Out of Vocabulary Word，OOV Word|hyperpage}{3
53
}
\indexentry{Out of Vocabulary Word，OOV Word|hyperpage}{3
45
}
\indexentry{子词切分|hyperpage}{3
53
}
\indexentry{子词切分|hyperpage}{3
45
}
\indexentry{Sub-word Segmentation|hyperpage}{3
53
}
\indexentry{Sub-word Segmentation|hyperpage}{3
45
}
\indexentry{标准化|hyperpage}{3
53
}
\indexentry{标准化|hyperpage}{3
45
}
\indexentry{Normalization|hyperpage}{3
53
}
\indexentry{Normalization|hyperpage}{3
45
}
\indexentry{数据清洗|hyperpage}{3
53
}
\indexentry{数据清洗|hyperpage}{3
45
}
\indexentry{Dada Cleaning|hyperpage}{3
53
}
\indexentry{Dada Cleaning|hyperpage}{3
45
}
\indexentry{数据选择|hyperpage}{3
55
}
\indexentry{数据选择|hyperpage}{3
47
}
\indexentry{Data Selection|hyperpage}{3
55
}
\indexentry{Data Selection|hyperpage}{3
47
}
\indexentry{数据过滤|hyperpage}{3
55
}
\indexentry{数据过滤|hyperpage}{3
47
}
\indexentry{Data Filtering|hyperpage}{3
55
}
\indexentry{Data Filtering|hyperpage}{3
47
}
\indexentry{开放词表|hyperpage}{35
8
}
\indexentry{开放词表|hyperpage}{35
0
}
\indexentry{Open-Vocabulary|hyperpage}{35
8
}
\indexentry{Open-Vocabulary|hyperpage}{35
0
}
\indexentry{子词|hyperpage}{35
9
}
\indexentry{子词|hyperpage}{35
1
}
\indexentry{Sub-word|hyperpage}{35
9
}
\indexentry{Sub-word|hyperpage}{35
1
}
\indexentry{字节对编码|hyperpage}{35
9
}
\indexentry{字节对编码|hyperpage}{35
1
}
\indexentry{双字节编码|hyperpage}{35
9
}
\indexentry{双字节编码|hyperpage}{35
1
}
\indexentry{Byte Pair Encoding，BPE|hyperpage}{35
9
}
\indexentry{Byte Pair Encoding，BPE|hyperpage}{35
1
}
\indexentry{正则化|hyperpage}{3
62
}
\indexentry{正则化|hyperpage}{3
54
}
\indexentry{Regularization|hyperpage}{3
62
}
\indexentry{Regularization|hyperpage}{3
54
}
\indexentry{过拟合问题|hyperpage}{3
62
}
\indexentry{过拟合问题|hyperpage}{3
54
}
\indexentry{Overfitting Problem|hyperpage}{3
62
}
\indexentry{Overfitting Problem|hyperpage}{3
54
}
\indexentry{反问题|hyperpage}{3
62
}
\indexentry{反问题|hyperpage}{3
54
}
\indexentry{Inverse Problem|hyperpage}{3
62
}
\indexentry{Inverse Problem|hyperpage}{3
54
}
\indexentry{适定的|hyperpage}{3
63
}
\indexentry{适定的|hyperpage}{3
54
}
\indexentry{Well-posed|hyperpage}{3
63
}
\indexentry{Well-posed|hyperpage}{3
54
}
\indexentry{不适定问题|hyperpage}{3
63
}
\indexentry{不适定问题|hyperpage}{3
54
}
\indexentry{Ill-posed Problem|hyperpage}{3
63
}
\indexentry{Ill-posed Problem|hyperpage}{3
54
}
\indexentry{降噪|hyperpage}{3
63
}
\indexentry{降噪|hyperpage}{3
55
}
\indexentry{Denoising|hyperpage}{3
63
}
\indexentry{Denoising|hyperpage}{3
55
}
\indexentry{泛化|hyperpage}{3
64
}
\indexentry{泛化|hyperpage}{3
55
}
\indexentry{Generalization|hyperpage}{3
64
}
\indexentry{Generalization|hyperpage}{3
55
}
\indexentry{标签平滑|hyperpage}{3
65
}
\indexentry{标签平滑|hyperpage}{3
57
}
\indexentry{Label Smoothing|hyperpage}{3
65
}
\indexentry{Label Smoothing|hyperpage}{3
57
}
\indexentry{相互适应|hyperpage}{3
66
}
\indexentry{相互适应|hyperpage}{3
58
}
\indexentry{Co-Adaptation|hyperpage}{3
66
}
\indexentry{Co-Adaptation|hyperpage}{3
58
}
\indexentry{集成学习|hyperpage}{3
68
}
\indexentry{集成学习|hyperpage}{3
59
}
\indexentry{Ensemble Learning|hyperpage}{3
68
}
\indexentry{Ensemble Learning|hyperpage}{3
59
}
\indexentry{容量|hyperpage}{36
9
}
\indexentry{容量|hyperpage}{36
0
}
\indexentry{Capacity|hyperpage}{36
9
}
\indexentry{Capacity|hyperpage}{36
0
}
\indexentry{宽残差网络|hyperpage}{36
9
}
\indexentry{宽残差网络|hyperpage}{36
1
}
\indexentry{Wide Residual Network|hyperpage}{36
9
}
\indexentry{Wide Residual Network|hyperpage}{36
1
}
\indexentry{探测任务|hyperpage}{3
71
}
\indexentry{探测任务|hyperpage}{3
62
}
\indexentry{Probing Task|hyperpage}{3
71
}
\indexentry{Probing Task|hyperpage}{3
62
}
\indexentry{表面信息|hyperpage}{3
71
}
\indexentry{表面信息|hyperpage}{3
62
}
\indexentry{Surface Information|hyperpage}{3
71
}
\indexentry{Surface Information|hyperpage}{3
62
}
\indexentry{语法信息|hyperpage}{3
71
}
\indexentry{语法信息|hyperpage}{3
62
}
\indexentry{Syntactic Information|hyperpage}{3
71
}
\indexentry{Syntactic Information|hyperpage}{3
62
}
\indexentry{语义信息|hyperpage}{3
71
}
\indexentry{语义信息|hyperpage}{3
62
}
\indexentry{Semantic Information|hyperpage}{3
71
}
\indexentry{Semantic Information|hyperpage}{3
62
}
\indexentry{词嵌入|hyperpage}{3
71
}
\indexentry{词嵌入|hyperpage}{3
62
}
\indexentry{Embedding|hyperpage}{3
71
}
\indexentry{Embedding|hyperpage}{3
62
}
\indexentry{数据并行|hyperpage}{3
72
}
\indexentry{数据并行|hyperpage}{3
63
}
\indexentry{Data Parallelism|hyperpage}{3
72
}
\indexentry{Data Parallelism|hyperpage}{3
63
}
\indexentry{模型并行|hyperpage}{3
72
}
\indexentry{模型并行|hyperpage}{3
63
}
\indexentry{Model Parallelism|hyperpage}{3
72
}
\indexentry{Model Parallelism|hyperpage}{3
63
}
\indexentry{小批量训练|hyperpage}{3
72
}
\indexentry{小批量训练|hyperpage}{3
63
}
\indexentry{Mini-batch Training|hyperpage}{3
72
}
\indexentry{Mini-batch Training|hyperpage}{3
63
}
\indexentry{课程学习|hyperpage}{3
74
}
\indexentry{课程学习|hyperpage}{3
65
}
\indexentry{Curriculum Learning|hyperpage}{3
74
}
\indexentry{Curriculum Learning|hyperpage}{3
65
}
\indexentry{推断|hyperpage}{3
75
}
\indexentry{推断|hyperpage}{3
66
}
\indexentry{Inference|hyperpage}{3
75
}
\indexentry{Inference|hyperpage}{3
66
}
\indexentry{解码|hyperpage}{3
75
}
\indexentry{解码|hyperpage}{3
66
}
\indexentry{Decoding|hyperpage}{3
75
}
\indexentry{Decoding|hyperpage}{3
66
}
\indexentry{准确性|hyperpage}{3
75
}
\indexentry{准确性|hyperpage}{3
66
}
\indexentry{Accuracy|hyperpage}{3
75
}
\indexentry{Accuracy|hyperpage}{3
66
}
\indexentry{时延|hyperpage}{3
75
}
\indexentry{时延|hyperpage}{3
66
}
\indexentry{Latency|hyperpage}{3
75
}
\indexentry{Latency|hyperpage}{3
66
}
\indexentry{时延|hyperpage}{3
75
}
\indexentry{时延|hyperpage}{3
66
}
\indexentry{Memory|hyperpage}{3
75
}
\indexentry{Memory|hyperpage}{3
66
}
\indexentry{搜索错误|hyperpage}{3
75
}
\indexentry{搜索错误|hyperpage}{3
66
}
\indexentry{Search Error|hyperpage}{3
75
}
\indexentry{Search Error|hyperpage}{3
66
}
\indexentry{模型错误|hyperpage}{3
75
}
\indexentry{模型错误|hyperpage}{3
66
}
\indexentry{Modeling Error|hyperpage}{3
75
}
\indexentry{Modeling Error|hyperpage}{3
66
}
\indexentry{重排序|hyperpage}{3
77
}
\indexentry{重排序|hyperpage}{3
68
}
\indexentry{Re-ranking|hyperpage}{3
77
}
\indexentry{Re-ranking|hyperpage}{3
68
}
\indexentry{双向推断|hyperpage}{3
77
}
\indexentry{双向推断|hyperpage}{3
68
}
\indexentry{Bidirectional Inference|hyperpage}{3
77
}
\indexentry{Bidirectional Inference|hyperpage}{3
68
}
\indexentry{批量推断|hyperpage}{3
8
1}
\indexentry{批量推断|hyperpage}{3
7
1}
\indexentry{Batch Inference|hyperpage}{3
8
1}
\indexentry{Batch Inference|hyperpage}{3
7
1}
\indexentry{批量处理|hyperpage}{3
8
1}
\indexentry{批量处理|hyperpage}{3
7
1}
\indexentry{Batching|hyperpage}{3
8
1}
\indexentry{Batching|hyperpage}{3
7
1}
\indexentry{二值网络|hyperpage}{3
8
3}
\indexentry{二值网络|hyperpage}{3
7
3}
\indexentry{Binarized Neural Networks|hyperpage}{3
8
3}
\indexentry{Binarized Neural Networks|hyperpage}{3
7
3}
\indexentry{自回归翻译|hyperpage}{3
8
3}
\indexentry{自回归翻译|hyperpage}{3
7
3}
\indexentry{Autoregressive Translation|hyperpage}{3
8
3}
\indexentry{Autoregressive Translation|hyperpage}{3
7
3}
\indexentry{非自回归翻译|hyperpage}{3
83
}
\indexentry{非自回归翻译|hyperpage}{3
74
}
\indexentry{
Regressive Translation|hyperpage}{383
}
\indexentry{
Non-Autoregressive Translation|hyperpage}{374
}
\indexentry{繁衍率|hyperpage}{3
83
}
\indexentry{繁衍率|hyperpage}{3
74
}
\indexentry{Fertility|hyperpage}{3
83
}
\indexentry{Fertility|hyperpage}{3
74
}
\indexentry{偏置|hyperpage}{3
8
5}
\indexentry{偏置|hyperpage}{3
7
5}
\indexentry{Bias|hyperpage}{3
8
5}
\indexentry{Bias|hyperpage}{3
7
5}
\indexentry{退化|hyperpage}{3
8
5}
\indexentry{退化|hyperpage}{3
7
5}
\indexentry{Degenerate|hyperpage}{3
8
5}
\indexentry{Degenerate|hyperpage}{3
7
5}
\indexentry{过翻译|hyperpage}{3
86
}
\indexentry{过翻译|hyperpage}{3
77
}
\indexentry{Over Translation|hyperpage}{3
86
}
\indexentry{Over Translation|hyperpage}{3
77
}
\indexentry{欠翻译|hyperpage}{3
86
}
\indexentry{欠翻译|hyperpage}{3
77
}
\indexentry{Under Translation|hyperpage}{3
86
}
\indexentry{Under Translation|hyperpage}{3
77
}
\indexentry{充分性|hyperpage}{3
8
7}
\indexentry{充分性|hyperpage}{3
7
7}
\indexentry{Adequacy|hyperpage}{3
8
7}
\indexentry{Adequacy|hyperpage}{3
7
7}
\indexentry{系统融合|hyperpage}{3
8
8}
\indexentry{系统融合|hyperpage}{3
7
8}
\indexentry{System Combination|hyperpage}{3
8
8}
\indexentry{System Combination|hyperpage}{3
7
8}
\indexentry{假设选择|hyperpage}{3
88
}
\indexentry{假设选择|hyperpage}{3
79
}
\indexentry{Hypothesis Selection|hyperpage}{3
88
}
\indexentry{Hypothesis Selection|hyperpage}{3
79
}
\indexentry{多样性|hyperpage}{3
88
}
\indexentry{多样性|hyperpage}{3
79
}
\indexentry{Diversity|hyperpage}{3
88
}
\indexentry{Diversity|hyperpage}{3
79
}
\indexentry{重排序|hyperpage}{3
8
9}
\indexentry{重排序|hyperpage}{3
7
9}
\indexentry{Re-ranking|hyperpage}{3
8
9}
\indexentry{Re-ranking|hyperpage}{3
7
9}
\indexentry{混淆网络|hyperpage}{3
90
}
\indexentry{混淆网络|hyperpage}{3
81
}
\indexentry{Confusion Network|hyperpage}{3
90
}
\indexentry{Confusion Network|hyperpage}{3
81
}
\indexentry{动态线性层聚合方法|hyperpage}{3
94
}
\indexentry{动态线性层聚合方法|hyperpage}{3
85
}
\indexentry{Dynamic Linear Combination of Layers，DLCL|hyperpage}{3
94
}
\indexentry{Dynamic Linear Combination of Layers，DLCL|hyperpage}{3
85
}
\indexentry{相互适应|hyperpage}{3
98
}
\indexentry{相互适应|hyperpage}{3
89
}
\indexentry{Co-adaptation|hyperpage}{3
98
}
\indexentry{Co-adaptation|hyperpage}{3
89
}
\indexentry{数据增强|hyperpage}{
40
1}
\indexentry{数据增强|hyperpage}{
39
1}
\indexentry{Data Augmentation|hyperpage}{
40
1}
\indexentry{Data Augmentation|hyperpage}{
39
1}
\indexentry{回译|hyperpage}{
40
1}
\indexentry{回译|hyperpage}{
39
1}
\indexentry{Back Translation|hyperpage}{
40
1}
\indexentry{Back Translation|hyperpage}{
39
1}
\indexentry{迭代式回译|hyperpage}{
401
}
\indexentry{迭代式回译|hyperpage}{
392
}
\indexentry{Iterative Back Translation|hyperpage}{
401
}
\indexentry{Iterative Back Translation|hyperpage}{
392
}
\indexentry{前向翻译|hyperpage}{
40
2}
\indexentry{前向翻译|hyperpage}{
39
2}
\indexentry{Forward Translation|hyperpage}{
40
2}
\indexentry{Forward Translation|hyperpage}{
39
2}
\indexentry{预训练|hyperpage}{
402
}
\indexentry{预训练|hyperpage}{
393
}
\indexentry{Pre-training|hyperpage}{
402
}
\indexentry{Pre-training|hyperpage}{
393
}
\indexentry{微调|hyperpage}{
402
}
\indexentry{微调|hyperpage}{
393
}
\indexentry{Fine-tuning|hyperpage}{
402
}
\indexentry{Fine-tuning|hyperpage}{
393
}
\indexentry{多任务学习|hyperpage}{
40
4}
\indexentry{多任务学习|hyperpage}{
39
4}
\indexentry{Multitask Learning|hyperpage}{
40
4}
\indexentry{Multitask Learning|hyperpage}{
39
4}
\indexentry{模型压缩|hyperpage}{
405
}
\indexentry{模型压缩|hyperpage}{
396
}
\indexentry{Model Compression|hyperpage}{
405
}
\indexentry{Model Compression|hyperpage}{
396
}
\indexentry{学习难度|hyperpage}{
405
}
\indexentry{学习难度|hyperpage}{
396
}
\indexentry{Learning Difficulty|hyperpage}{
40
6}
\indexentry{Learning Difficulty|hyperpage}{
39
6}
\indexentry{教师模型|hyperpage}{
40
6}
\indexentry{教师模型|hyperpage}{
39
6}
\indexentry{Teacher Model|hyperpage}{
40
6}
\indexentry{Teacher Model|hyperpage}{
39
6}
\indexentry{学生模型|hyperpage}{
406
}
\indexentry{学生模型|hyperpage}{
397
}
\indexentry{Student Model|hyperpage}{
406
}
\indexentry{Student Model|hyperpage}{
397
}
\indexentry{基于单词的知识精炼|hyperpage}{
406
}
\indexentry{基于单词的知识精炼|hyperpage}{
397
}
\indexentry{Word-level Knowledge Distillation|hyperpage}{
406
}
\indexentry{Word-level Knowledge Distillation|hyperpage}{
397
}
\indexentry{基于序列的知识精炼|hyperpage}{
40
7}
\indexentry{基于序列的知识精炼|hyperpage}{
39
7}
\indexentry{Sequence-level Knowledge Distillation|hyperpage}{
40
7}
\indexentry{Sequence-level Knowledge Distillation|hyperpage}{
39
7}
\indexentry{中间层输出|hyperpage}{
40
8}
\indexentry{中间层输出|hyperpage}{
39
8}
\indexentry{Hint-based Knowledge Transfer|hyperpage}{
40
8}
\indexentry{Hint-based Knowledge Transfer|hyperpage}{
39
8}
\indexentry{注意力分布|hyperpage}{
40
8}
\indexentry{注意力分布|hyperpage}{
39
8}
\indexentry{Attention To Attention Transfer|hyperpage}{
40
8}
\indexentry{Attention To Attention Transfer|hyperpage}{
39
8}
\indexentry{循环一致性|hyperpage}{4
10
}
\indexentry{循环一致性|hyperpage}{4
01
}
\indexentry{Circle Consistency|hyperpage}{4
10
}
\indexentry{Circle Consistency|hyperpage}{4
01
}
\indexentry{翻译中回译|hyperpage}{4
11
}
\indexentry{翻译中回译|hyperpage}{4
02
}
\indexentry{On-the-fly Back-translation|hyperpage}{4
11
}
\indexentry{On-the-fly Back-translation|hyperpage}{4
02
}
\indexentry{网络结构搜索技术|hyperpage}{4
1
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\indexentry{网络结构搜索技术|hyperpage}{4
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Book/mt-book-xelatex.ptc
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\contentsline {subsubsection}{什么是知识精炼}{
405}{section*.500}
\contentsline {subsubsection}{什么是知识精炼}{
396}{section*.501}%
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\defcounter {refsection}{0}\relax
\contentsline {subsubsection}{知识精炼的基本方法}{
406}{section*.501}
\contentsline {subsubsection}{知识精炼的基本方法}{
397}{section*.502}%
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408}{section*.503}
\contentsline {subsubsection}{机器翻译中的知识精炼}{
398}{section*.504}%
\defcounter {refsection}{0}\relax
\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {7.5.4}双向训练}{
408}{subsection.7.5.4}
\contentsline {subsection}{\numberline {7.5.4}双向训练}{
399}{subsection.7.5.4}%
\defcounter {refsection}{0}\relax
\defcounter {refsection}{0}\relax
\contentsline {subsubsection}{有监督对偶学习}{4
10}{section*.505}
\contentsline {subsubsection}{有监督对偶学习}{4
00}{section*.506}%
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\defcounter {refsection}{0}\relax
\contentsline {subsubsection}{无监督对偶学习}{4
10}{section*.506}
\contentsline {subsubsection}{无监督对偶学习}{4
01}{section*.507}%
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11}{section*.508}
\contentsline {subsubsection}{翻译中回译}{4
02}{section*.509}%
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\defcounter {refsection}{0}\relax
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12}{section.7.6}
\contentsline {section}{\numberline {7.6}小结及深入阅读}{4
02}{section.7.6}%
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\defcounter {refsection}{0}\relax
\contentsline {part}{\@mypartnumtocformat {IV}{附录}}{4
17}{part.4}
\contentsline {part}{\@mypartnumtocformat {IV}{附录}}{4
05}{part.4}%
\ttl@stoptoc {default@3}
\ttl@stoptoc {default@3}
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\ttl@starttoc {default@4}
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19}{Appendix.1.A}
\contentsline {chapter}{\numberline {A}附录A}{4
07}{appendix.1.A}%
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19}{section.1.A.1}
\contentsline {section}{\numberline {A.1}基准数据集}{4
07}{section.1.A.1}%
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20}{section.1.A.2}
\contentsline {section}{\numberline {A.2}平行语料}{4
08}{section.1.A.2}%
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\defcounter {refsection}{0}\relax
\contentsline {section}{\numberline {A.3}相关工具}{4
21}{section.1.A.3}
\contentsline {section}{\numberline {A.3}相关工具}{4
09}{section.1.A.3}%
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\defcounter {refsection}{0}\relax
\contentsline {subsection}{\numberline {A.3.1}数据预处理工具}{4
21}{subsection.1.A.3.1}
\contentsline {subsection}{\numberline {A.3.1}数据预处理工具}{4
09}{subsection.1.A.3.1}%
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22}{subsection.1.A.3.2}
\contentsline {subsection}{\numberline {A.3.2}评价工具}{4
10}{subsection.1.A.3.2}%
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23}{Appendix.2.B}
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11}{appendix.2.B}%
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23}{section.2.B.1}
\contentsline {section}{\numberline {B.1}IBM模型3训练方法}{4
11}{section.2.B.1}%
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25}{section.2.B.2}
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13}{section.2.B.2}%
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27}{section.2.B.3}
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15}{section.2.B.3}%
\contentsfinish
\contentsfinish
Section03-Word-Based-Models/section03.tex
查看文件 @
bd87e7ad
...
@@ -909,12 +909,12 @@
...
@@ -909,12 +909,12 @@
\begin{itemize}
\begin{itemize}
\item
很多时候，我们有多个互译句对
$
(
\mathbf
{
s
}^{
[
1
]
}
,
\mathbf
{
t
}^{
[
1
]
}
)
,...,
(
\mathbf
{
s
}^{
[
n
]
}
,
\mathbf
{
t
}^{
[
n
]
}
)
$
，称之为
\alert
{
双语平行数据(语料)
}
。翻译概率可以被定义为
\item
如果有多个互译句对
$
\{
(
\mathbf
{
s
}^{
[
1
]
}
,
\mathbf
{
t
}^{
[
1
]
}
)
,...,
(
\mathbf
{
s
}^{
[
K
]
}
,
\mathbf
{
t
}^{
[
K
]
}
)
\}
$
，称之为
\alert
{
双语平行数据(语料)
}
。翻译概率可以被定义为
\vspace
{
-1em
}
\vspace
{
-1em
}
\begin{eqnarray}
\begin{eqnarray}
\textrm
{
P
}
(x,y)
&
=
&
\frac
{
\sum
_{
i=1
}^{
n
}
c(x,y;
\mathbf
{
s
}^{
[i]
}
,
\mathbf
{
t
}^{
[i]
}
)
}{
\sum
_{
i=1
}^{
n
}
\sum
_{
x',y'
}
c(x',y';
\mathbf
{
s
}^{
[i]
}
,
\mathbf
{
t
}^{
[i
]
}
)
}
\nonumber
\textrm
{
P
}
(x,y)
&
=
&
\frac
{
\sum
_{
k=1
}^{
K
}
c(x,y;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
}{
\sum
_{
k=1
}^{
K
}
\sum
_{
x',y'
}
c(x',y';
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k
]
}
)
}
\nonumber
\end{eqnarray}
\end{eqnarray}
\item
<2-> 说白了就是计算
$
(
x,y
)
$
的频次时，在每个句子上累加
\item
<2-> 说白了就是计算
$
(
x,y
)
$
的频次时，在每个句子上累加
...
@@ -1414,7 +1414,7 @@ $m$ & $n$ & $n^m \cdot m!$ \\ \hline
...
@@ -1414,7 +1414,7 @@ $m$ & $n$ & $n^m \cdot m!$ \\ \hline
\node
[anchor=north west,inner sep=2pt,align=left] (line4) at ([yshift=-1pt]line3.south west)
{
\textrm
{
3:
\textbf
{
for
}
$
i
$
in
$
[
1
,m
]
$
\textbf
{
do
}}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line4) at ([yshift=-1pt]line3.south west)
{
\textrm
{
3:
\textbf
{
for
}
$
i
$
in
$
[
1
,m
]
$
\textbf
{
do
}}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line5) at ([yshift=-1pt]line4.south west)
{
\textrm
{
4:
\hspace
{
1em
}
$
h
=
\phi
$}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line5) at ([yshift=-1pt]line4.south west)
{
\textrm
{
4:
\hspace
{
1em
}
$
h
=
\phi
$}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line6) at ([yshift=-1pt]line5.south west)
{
\textrm
{
5:
\hspace
{
1em
}
\textbf
{
foreach
}
$
j
$
in
$
[
1
,m
]
$
\textbf
{
do
}}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line6) at ([yshift=-1pt]line5.south west)
{
\textrm
{
5:
\hspace
{
1em
}
\textbf
{
foreach
}
$
j
$
in
$
[
1
,m
]
$
\textbf
{
do
}}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line7) at ([yshift=-1pt]line6.south west)
{
\textrm
{
6:
\hspace
{
2em
}
\textbf
{
if
}
$
used
[
j
]=
$
\textbf
{
tru
e
}
\textbf
{
then
}}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line7) at ([yshift=-1pt]line6.south west)
{
\textrm
{
6:
\hspace
{
2em
}
\textbf
{
if
}
$
used
[
j
]=
$
\textbf
{
fals
e
}
\textbf
{
then
}}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line8) at ([yshift=-1pt]line7.south west)
{
\textrm
{
7:
\hspace
{
3em
}
$
h
=
h
\cup
\textrm
{
\textsc
{
Join
}}
(
best,
\pi
[
j
])
$}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line8) at ([yshift=-1pt]line7.south west)
{
\textrm
{
7:
\hspace
{
3em
}
$
h
=
h
\cup
\textrm
{
\textsc
{
Join
}}
(
best,
\pi
[
j
])
$}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line9) at ([yshift=-1pt]line8.south west)
{
\textrm
{
8:
\hspace
{
1em
}
$
best
=
\textrm
{
\textsc
{
PruneForTop
1
}}
(
h
)
$}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line9) at ([yshift=-1pt]line8.south west)
{
\textrm
{
8:
\hspace
{
1em
}
$
best
=
\textrm
{
\textsc
{
PruneForTop
1
}}
(
h
)
$}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line10) at ([yshift=-1pt]line9.south west)
{
\textrm
{
9:
\hspace
{
1em
}
$
used
[
best.j
]
=
\textrm
{
\textsc
{
\textbf
{
true
}}}$}}
;
\node
[anchor=north west,inner sep=2pt,align=left] (line10) at ([yshift=-1pt]line9.south west)
{
\textrm
{
9:
\hspace
{
1em
}
$
used
[
best.j
]
=
\textrm
{
\textsc
{
\textbf
{
true
}}}$}}
;
...
@@ -2395,7 +2395,7 @@ $m$ & $n$ & $n^m \cdot m!$ \\ \hline
...
@@ -2395,7 +2395,7 @@ $m$ & $n$ & $n^m \cdot m!$ \\ \hline
\item
\textbf
{
翻译模型参数估计
}
- 计算
$
\textrm
{
P
}
(
\mathbf
{
s
}
|
\mathbf
{
t
}
)
$
所需的参数
\item
\textbf
{
翻译模型参数估计
}
- 计算
$
\textrm
{
P
}
(
\mathbf
{
s
}
|
\mathbf
{
t
}
)
$
所需的参数
\end{itemize}
\end{itemize}
\vspace
{
0.5em
}
\vspace
{
0.5em
}
\item
<2->
\textbf
{
IBM模型的假设
}
：
$
\mathbf
{
s
}
=
s
_
1
...s
_
m
$
和
$
\mathbf
{
t
}
=
t
_
1
...t
_
n
$
之间有单词一级的对应，称作
\alert
{
单词对齐
}
或者
\alert
{
词对齐
}
。此外：
\item
<2->
\textbf
{
IBM模型的假设
}
：
$
\mathbf
{
s
}
=
s
_
1
...s
_
m
$
和
$
\mathbf
{
t
}
=
t
_
1
...t
_
l
$
之间有单词一级的对应，称作
\alert
{
单词对齐
}
或者
\alert
{
词对齐
}
。此外：
\begin{itemize}
\begin{itemize}
\item
\textbf
{
约束
}
：一个源语言单词只能对应一个目标语单词
\item
\textbf
{
约束
}
：一个源语言单词只能对应一个目标语单词
\vspace
{
0.5em
}
\vspace
{
0.5em
}
...
@@ -2792,11 +2792,11 @@ $\mathbf{s}$ = 在 桌子 上 \ \ \ \ \ $\mathbf{t}$ = $t_0$ on the table \ \ \
...
@@ -2792,11 +2792,11 @@ $\mathbf{s}$ = 在 桌子 上 \ \ \ \ \ $\mathbf{t}$ = $t_0$ on the table \ \ \
\textrm
{
P
}
(
\mathbf
{
s
}
,
\mathbf
{
a
}
|
\mathbf
{
t
}
)
&
=
&
\textrm
{
P
}
(m|
\mathbf
{
t
}
)
\prod\limits
_{
j=1
}^{
m
}
\textrm
{
P
}
(a
_
j|a
_{
1
}^{
j-1
}
,s
_{
1
}^{
j-1
}
,m,
\mathbf
{
t
}
)
\textrm
{
P
}
(s
_
j|a
_{
1
}^{
j
}
,s
_{
1
}^{
j-1
}
,m,
\mathbf
{
t
}
)
\nonumber
\\
\textrm
{
P
}
(
\mathbf
{
s
}
,
\mathbf
{
a
}
|
\mathbf
{
t
}
)
&
=
&
\textrm
{
P
}
(m|
\mathbf
{
t
}
)
\prod\limits
_{
j=1
}^{
m
}
\textrm
{
P
}
(a
_
j|a
_{
1
}^{
j-1
}
,s
_{
1
}^{
j-1
}
,m,
\mathbf
{
t
}
)
\textrm
{
P
}
(s
_
j|a
_{
1
}^{
j
}
,s
_{
1
}^{
j-1
}
,m,
\mathbf
{
t
}
)
\nonumber
\\
&
\visible
<2->
{
=
}
&
\visible
<2->
{
\textrm
{
P
}
(m=3
\mid
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\visible
<3->
{
\times
}
\nonumber
\\
&
\visible
<2->
{
=
}
&
\visible
<2->
{
\textrm
{
P
}
(m=3
\mid
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\visible
<3->
{
\times
}
\nonumber
\\
&
&
\visible
<3->
{
\textrm
{
P
}
(a
_
1=0
\mid
\phi
,
\phi
,3,
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\visible
<4->
{
\times
}
\nonumber
\\
&
&
\visible
<3->
{
\textrm
{
P
}
(a
_
1=0
\mid
\phi
,
\phi
,3,
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\visible
<4->
{
\times
}
\nonumber
\\
&
&
\visible
<4->
{
\textrm
{
P
}
(
f
_
1=
\textrm
{
在
}
\mid
\textrm
{
\{
1-0
\}
}
,
\phi
,3,
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\visible
<5->
{
\times
}
\nonumber
\\
&
&
\visible
<4->
{
\textrm
{
P
}
(
s
_
1=
\textrm
{
在
}
\mid
\textrm
{
\{
1-0
\}
}
,
\phi
,3,
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\visible
<5->
{
\times
}
\nonumber
\\
&
&
\visible
<5->
{
\textrm
{
P
}
(a
_
2=3
\mid
\textrm
{
\{
1-0
\}
}
,
\textrm
{
'在'
}
,3,
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\visible
<6->
{
\times
}
\nonumber
\\
&
&
\visible
<5->
{
\textrm
{
P
}
(a
_
2=3
\mid
\textrm
{
\{
1-0
\}
}
,
\textrm
{
'在'
}
,3,
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\visible
<6->
{
\times
}
\nonumber
\\
&
&
\visible
<6->
{
\textrm
{
P
}
(
f
_
2=
\textrm
{
桌子
}
\mid
\textrm
{
\{
1-0,2-3
\}
}
,
\textrm
{
'在'
}
,3,
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\visible
<7->
{
\times
}
\nonumber
\\
&
&
\visible
<6->
{
\textrm
{
P
}
(
s
_
2=
\textrm
{
桌子
}
\mid
\textrm
{
\{
1-0,2-3
\}
}
,
\textrm
{
'在'
}
,3,
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\visible
<7->
{
\times
}
\nonumber
\\
&
&
\visible
<7->
{
\textrm
{
P
}
(a
_
3=1
\mid
\textrm
{
\{
1-0,2-3
\}
}
,
\textrm
{
'在 桌子'
}
,3,
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\visible
<8->
{
\times
}
\nonumber
\\
&
&
\visible
<7->
{
\textrm
{
P
}
(a
_
3=1
\mid
\textrm
{
\{
1-0,2-3
\}
}
,
\textrm
{
'在 桌子'
}
,3,
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\visible
<8->
{
\times
}
\nonumber
\\
&
&
\visible
<8->
{
\textrm
{
P
}
(
f
_
3=
\textrm
{
上
}
\mid
\textrm
{
\{
1-0,2-3,3-1
\}
}
,
\textrm
{
'在 桌子'
}
,3,
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\nonumber
&
&
\visible
<8->
{
\textrm
{
P
}
(
s
_
3=
\textrm
{
上
}
\mid
\textrm
{
\{
1-0,2-3,3-1
\}
}
,
\textrm
{
'在 桌子'
}
,3,
\textrm
{
'
$
t
_
0
$
on the table'
}
)
}
\nonumber
\end{eqnarray}
\end{eqnarray}
}
}
...
@@ -3730,7 +3730,7 @@ $\mathbf{s}$ = 在 桌子 上 \ \ \ \ \ $\mathbf{t}$ = $t_0$ on the table \ \ \
...
@@ -3730,7 +3730,7 @@ $\mathbf{s}$ = 在 桌子 上 \ \ \ \ \ $\mathbf{t}$ = $t_0$ on the table \ \ \
{
\small
{
\small
\begin{eqnarray}
\begin{eqnarray}
L(f,
\lambda
)
&
=
&
\frac
{
\epsilon
}{
(l+1)
^{
m
}}
\prod\limits
_{
j=1
}^{
m
}
\sum\limits
_{
i=0
}^{
l
}
\prod\limits
_{
j=1
}^{
m
}
f(s
_
j|t
_
i) -
\nonumber
\\
L(f,
\lambda
)
&
=
&
\frac
{
\epsilon
}{
(l+1)
^{
m
}}
\prod\limits
_{
j=1
}^{
m
}
\sum\limits
_{
i=0
}^{
l
}
f(s
_
j|t
_
i) -
\nonumber
\\
&
&
\sum
_{
t
_
y
}
\lambda
_{
t
_
y
}
(
\sum
_{
s
_
x
}
f(s
_
x|t
_
y) -1)
\nonumber
&
&
\sum
_{
t
_
y
}
\lambda
_{
t
_
y
}
(
\sum
_{
s
_
x
}
f(s
_
x|t
_
y) -1)
\nonumber
\end{eqnarray}
\end{eqnarray}
}
}
...
@@ -4190,9 +4190,9 @@ f(s_u|t_v) & = & \lambda_{t_v}^{-1} \cdot \textrm{P}(\mathbf{s}|\mathbf{t}) \cdo
...
@@ -4190,9 +4190,9 @@ f(s_u|t_v) & = & \lambda_{t_v}^{-1} \cdot \textrm{P}(\mathbf{s}|\mathbf{t}) \cdo
%%% scale it up to the full corpus
%%% scale it up to the full corpus
\begin{frame}
{
在整个数据集上计算
}
\begin{frame}
{
在整个数据集上计算
}
\begin{itemize}
\begin{itemize}
\item
\textbf
{
更真实的情况
}
：我们拥有一系列互译的句对（称作
\alert
{
平行语料
}
），记为
$
\{
(
\mathbf
{
s
}^{
[
1
]
}
,
\mathbf
{
t
}^{
[
1
]
}
)
,
(
\mathbf
{
s
}^{
[
2
]
}
,
\mathbf
{
t
}^{
[
2
]
}
)
,...,
(
\mathbf
{
s
}^{
[
N
]
}
,
\mathbf
{
t
}^{
[
N
]
}
)
\}
$
。对于这
$
N
$
个训练用句对，定义
$
f
(
s
_
u|t
_
v
)
$
的期望频次为
\item
\textbf
{
更真实的情况
}
：我们拥有一系列互译的句对（称作
\alert
{
平行语料
}
），记为
$
\{
(
\mathbf
{
s
}^{
[
1
]
}
,
\mathbf
{
t
}^{
[
1
]
}
)
,
(
\mathbf
{
s
}^{
[
2
]
}
,
\mathbf
{
t
}^{
[
2
]
}
)
,...,
(
\mathbf
{
s
}^{
[
K
]
}
,
\mathbf
{
t
}^{
[
K
]
}
)
\}
$
。对于这
$
K
$
个训练用句对，定义
$
f
(
s
_
u|t
_
v
)
$
的期望频次为
\begin{displaymath}
\begin{displaymath}
c
_{
\mathbb
{
E
}}
(s
_
u|t
_
v) =
\sum
_{
i=1
}^{
N
}
c
_{
\mathbb
{
E
}}
(s
_
u|t
_
v;
\mathbf
{
s
}^{
[i]
}
,
\mathbf
{
t
}^{
[i
]
}
)
c
_{
\mathbb
{
E
}}
(s
_
u|t
_
v) =
\sum
_{
k=1
}^{
K
}
c
_{
\mathbb
{
E
}}
(s
_
u|t
_
v;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k
]
}
)
\end{displaymath}
\end{displaymath}
\item
<2->
\textbf
{
于是
}
\item
<2->
\textbf
{
于是
}
\begin{center}
\begin{center}
...
@@ -4200,8 +4200,8 @@ f(s_u|t_v) & = & \lambda_{t_v}^{-1} \cdot \textrm{P}(\mathbf{s}|\mathbf{t}) \cdo
...
@@ -4200,8 +4200,8 @@ f(s_u|t_v) & = & \lambda_{t_v}^{-1} \cdot \textrm{P}(\mathbf{s}|\mathbf{t}) \cdo
\node
[anchor=west,inner sep=2pt] (eq1) at (0,0)
{$
f
(
s
_
u|t
_
v
)
$}
;
\node
[anchor=west,inner sep=2pt] (eq1) at (0,0)
{$
f
(
s
_
u|t
_
v
)
$}
;
\node
[anchor=west] (eq2) at (eq1.east)
{$
=
$
\
}
;
\node
[anchor=west] (eq2) at (eq1.east)
{$
=
$
\
}
;
\draw
[-] ([xshift=0.3em]eq2.east) -- ([xshift=11.6em]eq2.east);
\draw
[-] ([xshift=0.3em]eq2.east) -- ([xshift=11.6em]eq2.east);
\node
[anchor=south west] (eq3) at ([xshift=1em]eq2.east)
{$
\sum
_{
i
=
1
}^{
N
}
c
_{
\mathbb
{
E
}}
(
s
_
u|t
_
v;
\mathbf
{
s
}^{
[
i
]
}
,
\mathbf
{
t
}^{
[
i
]
}
)
$}
;
\node
[anchor=south west] (eq3) at ([xshift=1em]eq2.east)
{$
\sum
_{
k
=
1
}^{
K
}
c
_{
\mathbb
{
E
}}
(
s
_
u|t
_
v;
\mathbf
{
s
}^{
[
k
]
}
,
\mathbf
{
t
}^{
[
k
]
}
)
$}
;
\node
[anchor=north west] (eq4) at (eq2.east)
{$
\sum
_{
s
_
u
}
\sum
_{
i
=
1
}^{
N
}
c
_{
\mathbb
{
E
}}
(
s
_
u|t
_
v;
\mathbf
{
s
}^{
[
i
]
}
,
\mathbf
{
t
}^{
[
i
]
}
)
$}
;
\node
[anchor=north west] (eq4) at (eq2.east)
{$
\sum
_{
s
_
u
}
\sum
_{
k
=
1
}^{
K
}
c
_{
\mathbb
{
E
}}
(
s
_
u|t
_
v;
\mathbf
{
s
}^{
[
k
]
}
,
\mathbf
{
t
}^{
[
k
]
}
)
$}
;
\visible
<4->
{
\visible
<4->
{
\node
[anchor=south] (label1) at ([yshift=-6em,xshift=3em]eq1.north west)
{
利用这个公式计算
}
;
\node
[anchor=south] (label1) at ([yshift=-6em,xshift=3em]eq1.north west)
{
利用这个公式计算
}
;
...
@@ -4250,16 +4250,16 @@ f(s_u|t_v) & = & \lambda_{t_v}^{-1} \cdot \textrm{P}(\mathbf{s}|\mathbf{t}) \cdo
...
@@ -4250,16 +4250,16 @@ f(s_u|t_v) & = & \lambda_{t_v}^{-1} \cdot \textrm{P}(\mathbf{s}|\mathbf{t}) \cdo
\label
{
ibmtraining
}
\label
{
ibmtraining
}
\begin{beamerboxesrounded}
[upper=uppercolblue,lower=lowercolblue,shadow=true]
{
IBM模型1的训练（EM算法）
}
\begin{beamerboxesrounded}
[upper=uppercolblue,lower=lowercolblue,shadow=true]
{
IBM模型1的训练（EM算法）
}
输入: 平行语料
$
\{
(
\mathbf
{
s
}^{
[
1
]
}
,
\mathbf
{
t
}^{
[
1
]
}
)
,...,
(
\mathbf
{
s
}^{
[
N
]
}
,
\mathbf
{
t
}^{
[
N
]
}
)
\}
$
\\
输入: 平行语料
$
\{
(
\mathbf
{
s
}^{
[
1
]
}
,
\mathbf
{
t
}^{
[
1
]
}
)
,...,
(
\mathbf
{
s
}^{
[
K
]
}
,
\mathbf
{
t
}^{
[
K
]
}
)
\}
$
\\
输出：参数
$
f
(
\cdot
|
\cdot
)
$
的最优值
\\
输出：参数
$
f
(
\cdot
|
\cdot
)
$
的最优值
\\
1:
\textbf
{
Function
}
\textsc
{
TrainItWithEM
}
(
$
\{
(
\mathbf
{
s
}^{
[
1
]
}
,
\mathbf
{
t
}^{
[
1
]
}
)
,...,
(
\mathbf
{
s
}^{
[
N
]
}
,
\mathbf
{
t
}^{
[
N
]
}
)
\}
$
)
\\
1:
\textbf
{
Function
}
\textsc
{
TrainItWithEM
}
(
$
\{
(
\mathbf
{
s
}^{
[
1
]
}
,
\mathbf
{
t
}^{
[
1
]
}
)
,...,
(
\mathbf
{
s
}^{
[
K
]
}
,
\mathbf
{
t
}^{
[
K
]
}
)
\}
$
)
\\
2:
\ \
Initialize
$
f
(
\cdot
|
\cdot
)
$
\hspace
{
5em
}
$
\rhd
$
比如给
$
f
(
\cdot
|
\cdot
)
$
一个均匀分布
\\
2:
\ \
Initialize
$
f
(
\cdot
|
\cdot
)
$
\hspace
{
5em
}
$
\rhd
$
比如给
$
f
(
\cdot
|
\cdot
)
$
一个均匀分布
\\
3:
\ \
Loop until
$
f
(
\cdot
|
\cdot
)
$
converges
\\
3:
\ \
Loop until
$
f
(
\cdot
|
\cdot
)
$
converges
\\
4:
\ \ \ \ \textbf
{
foreach
}
$
k
=
1
$
to
$
N
$
\textbf
{
do
}
\\
4:
\ \ \ \ \textbf
{
foreach
}
$
k
=
1
$
to
$
K
$
\textbf
{
do
}
\\
5:
\ \ \ \ \ \ \ \footnotesize
{$
c
_{
\mathbb
{
E
}}
(
s
_
u|t
_
v;
\mathbf
{
s
}^{
[
k
]
}
,
\mathbf
{
t
}^{
[
k
]
}
)
=
\sum\limits
_{
j
=
1
}^{
|
\mathbf
{
s
}^{
[
k
]
}
|
}
\delta
(
s
_
j,s
_
u
)
\sum\limits
_{
i
=
0
}^{
|
\mathbf
{
t
}^{
[
k
]
}
|
}
\delta
(
t
_
i,t
_
v
)
\cdot
\frac
{
f
(
s
_
u|t
_
v
)
}{
\sum
_{
i
=
0
}^{
l
}
f
(
s
_
u|t
_
i
)
}$}
\normalsize
{}
\\
5:
\ \ \ \ \ \ \ \footnotesize
{$
c
_{
\mathbb
{
E
}}
(
s
_
u|t
_
v;
\mathbf
{
s
}^{
[
k
]
}
,
\mathbf
{
t
}^{
[
k
]
}
)
=
\sum\limits
_{
j
=
1
}^{
|
\mathbf
{
s
}^{
[
k
]
}
|
}
\delta
(
s
_
j,s
_
u
)
\sum\limits
_{
i
=
0
}^{
|
\mathbf
{
t
}^{
[
k
]
}
|
}
\delta
(
t
_
i,t
_
v
)
\cdot
\frac
{
f
(
s
_
u|t
_
v
)
}{
\sum
_{
i
=
0
}^{
l
}
f
(
s
_
u|t
_
i
)
}$}
\normalsize
{}
\\
6:
\ \ \ \ \textbf
{
foreach
}
$
t
_
v
$
appears at least one of
$
\{\mathbf
{
t
}^{
[
1
]
}
,...,
\mathbf
{
t
}^{
[
N
]
}
\}
$
\textbf
{
do
}
\\
6:
\ \ \ \ \textbf
{
foreach
}
$
t
_
v
$
appears at least one of
$
\{\mathbf
{
t
}^{
[
1
]
}
,...,
\mathbf
{
t
}^{
[
K
]
}
\}
$
\textbf
{
do
}
\\
7:
\ \ \ \ \ \ \
$
\lambda
_{
t
_
v
}^{
'
}
=
\sum
_{
s
_
u
}
\sum
_{
k
=
1
}^{
N
}
c
_{
\mathbb
{
E
}}
(
s
_
u|t
_
v;
\mathbf
{
s
}^{
[
k
]
}
,
\mathbf
{
t
}^{
[
k
]
}
)
$
\\
7:
\ \ \ \ \ \ \
$
\lambda
_{
t
_
v
}^{
'
}
=
\sum
_{
s
_
u
}
\sum
_{
k
=
1
}^{
N
}
c
_{
\mathbb
{
E
}}
(
s
_
u|t
_
v;
\mathbf
{
s
}^{
[
k
]
}
,
\mathbf
{
t
}^{
[
k
]
}
)
$
\\
8:
\ \ \ \ \ \ \ \textbf
{
foreach
}
$
s
_
u
$
appears at least one of
$
\{\mathbf
{
s
}^{
[
1
]
}
,...,
\mathbf
{
s
}^{
[
N
]
}
\}
$
\textbf
{
do
}
\\
8:
\ \ \ \ \ \ \ \textbf
{
foreach
}
$
s
_
u
$
appears at least one of
$
\{\mathbf
{
s
}^{
[
1
]
}
,...,
\mathbf
{
s
}^{
[
K
]
}
\}
$
\textbf
{
do
}
\\
9:
\ \ \ \ \ \ \ \ \
$
f
(
s
_
u|t
_
v
)
=
\sum
_{
k
=
1
}^{
N
}
c
_{
\mathbb
{
E
}}
(
s
_
u|t
_
v;
\mathbf
{
s
}^{
[
k
]
}
,
\mathbf
{
t
}^{
[
k
]
}
)
\cdot
(
\lambda
_{
t
_
v
}^{
'
}
)
^{
-
1
}$
\\
9:
\ \ \ \ \ \ \ \ \
$
f
(
s
_
u|t
_
v
)
=
\sum
_{
k
=
1
}^{
N
}
c
_{
\mathbb
{
E
}}
(
s
_
u|t
_
v;
\mathbf
{
s
}^{
[
k
]
}
,
\mathbf
{
t
}^{
[
k
]
}
)
\cdot
(
\lambda
_{
t
_
v
}^{
'
}
)
^{
-
1
}$
\\
10:
\ \textbf
{
return
}
$
f
(
\cdot
|
\cdot
)
$
10:
\ \textbf
{
return
}
$
f
(
\cdot
|
\cdot
)
$
\end{beamerboxesrounded}
\end{beamerboxesrounded}
...
@@ -4287,8 +4287,8 @@ c_{\mathbb{E}}(i|j,m,l;\mathbf{s},\mathbf{t}) & = & \frac{f(s_j|t_i)a(i|j,m,l)}{
...
@@ -4287,8 +4287,8 @@ c_{\mathbb{E}}(i|j,m,l;\mathbf{s},\mathbf{t}) & = & \frac{f(s_j|t_i)a(i|j,m,l)}{
\end{eqnarray}
\end{eqnarray}
\item
\textbf
{
M-Step
}
\item
\textbf
{
M-Step
}
\begin{eqnarray}
\begin{eqnarray}
f(s
_
u|t
_
v)
&
=
&
\frac
{
\sum
_{
k=
0
}^{
K
}
c
_{
\mathbb
{
E
}}
(s
_
u|t
_
v;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
}{
\sum
_{
s
_
u
}
\sum
_{
k=0
}^{
K
}
c
_{
\mathbb
{
E
}}
(s
_
u|t
_
v;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
}
\nonumber
\\
f(s
_
u|t
_
v)
&
=
&
\frac
{
\sum
_{
k=
1
}^{
K
}
c
_{
\mathbb
{
E
}}
(s
_
u|t
_
v;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
}{
\sum
_{
s
_
u
}
\sum
_{
k=1
}^{
K
}
c
_{
\mathbb
{
E
}}
(s
_
u|t
_
v;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
}
\nonumber
\\
a(i|j,m,l)
&
=
&
\frac
{
\sum
_{
k=
0
}^{
K
}
c
_{
\mathbb
{
E
}}
(i|j;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
}{
\sum
_{
i
}
\sum
_{
k=0
}^{
K
}
c
_{
\mathbb
{
E
}}
(i|j;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
}
\nonumber
a(i|j,m,l)
&
=
&
\frac
{
\sum
_{
k=
1
}^{
K
}
c
_{
\mathbb
{
E
}}
(i|j;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
}{
\sum
_{
i
}
\sum
_{
k=1
}^{
K
}
c
_{
\mathbb
{
E
}}
(i|j;
\mathbf
{
s
}^{
[k]
}
,
\mathbf
{
t
}^{
[k]
}
)
}
\nonumber
\end{eqnarray}
\end{eqnarray}
\end{enumerate}
\end{enumerate}
\end{frame}
\end{frame}
...
...
Section05-Neural-Networks-and-Language-Modeling/section05.tex
查看文件 @
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@@ -541,7 +541,7 @@ GPT-2 (Transformer) & Radford et al. & 2019 & \alert{35.7}
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@@ -541,7 +541,7 @@ GPT-2 (Transformer) & Radford et al. & 2019 & \alert{35.7}
\end{itemize}
\end{itemize}
\item
<2->
\textbf
{
当然
}
，你是一个勇于实践的人
\item
<2->
\textbf
{
当然
}
，你是一个勇于实践的人
\begin{itemize}
\begin{itemize}
\item
方法很简单：不断地尝试，根据结
构
不断地调整权重
\item
方法很简单：不断地尝试，根据结
果
不断地调整权重
\item
<10-> 在进行了很多次实验后，发现了相对好的一组权重
\item
<10-> 在进行了很多次实验后，发现了相对好的一组权重
\end{itemize}
\end{itemize}
\end{itemize}
\end{itemize}
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@@ -1034,7 +1034,7 @@ T(\alpha \textbf{a}) & = & \alpha T(\textbf{a}) \nonumber
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@@ -1034,7 +1034,7 @@ T(\alpha \textbf{a}) & = & \alpha T(\textbf{a}) \nonumber
\visible
<3->
{
\visible
<3->
{
\node
[anchor=center,fill=green!20] (w2) at (w)
{
\Large
{$
\textbf
{
w
}$}}
;
\node
[anchor=center,fill=green!20] (w2) at (w)
{
\Large
{$
\textbf
{
w
}$}}
;
\node
[anchor=north,inner sep=1pt] (wlabel) at ([yshift=-0.7em]w.south)
{
\small
{
旋转(rotation)
}}
;
\node
[anchor=north,inner sep=1pt] (wlabel) at ([yshift=-0.7em]w.south)
{
\small
{
旋转(rotation)
、扩张(dilation)、挤压(squeeze)等
}}
;
\draw
[<-] ([yshift=-0.2em]w2.south) -- (wlabel.north);
\draw
[<-] ([yshift=-0.2em]w2.south) -- (wlabel.north);
\tikzstyle
{
neuron
}
= [rectangle,draw,thick,fill=red!30,red!35,minimum height=2em,minimum width=2em,font=
\small
]
\tikzstyle
{
neuron
}
= [rectangle,draw,thick,fill=red!30,red!35,minimum height=2em,minimum width=2em,font=
\small
]
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