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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 @@
\node
[anchor=west] (s1) at (0,0)
{{
我
}}
;
\node
[anchor=west] (s2) at ([xshift=2em]s1.east)
{{
对
}}
;
\node
[anchor=west] (s3) at ([xshift=2em]s2.east)
{{
你
}}
;
\node
[anchor=west] (s4) at ([xshift=2em]s3.east)
{{
表示
}}
;
\node
[anchor=west] (s4) at ([xshift=2em]s3.east)
{{
感到
}}
;
\node
[anchor=west] (s5) at ([xshift=2em]s4.east)
{{
满意
}}
;
\node
[anchor=south west] (sentlabel) at ([yshift=-0.5em]s1.north west)
{
\scriptsize
{
\sffamily\bfseries
{
\color
{
red
}{
待翻译句子(已经分词):
}}}}
;
...
...
@@ -38,7 +38,7 @@
\node
[anchor=north west,inner sep=1pt,fill=black] (tl31) at (t31.north west)
{
\tiny
{{
\color
{
white
}
\textbf
{
3
}}}}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3em] (t41) at ([yshift=-1em]s4.south)
{$
\phi
$}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3em] (t42) at ([yshift=-0.2em]t41.south)
{
show
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3em] (t42) at ([yshift=-0.2em]t41.south)
{
feel
}
;
\node
[anchor=north west,inner sep=1pt,fill=black] (tl41) at (t41.north west)
{
\tiny
{{
\color
{
white
}
\textbf
{
4
}}}}
;
\node
[anchor=north west,inner sep=1pt,fill=black] (tl42) at (t42.north west)
{
\tiny
{{
\color
{
white
}
\textbf
{
4
}}}}
;
...
...
Book/Chapter3/Figures/figure-noise-channel-model.tex
查看文件 @
bd87e7ad
...
...
@@ -9,7 +9,8 @@
\node
[draw,red,fill=red!10,thick,anchor=center,circle,inner sep=3.5pt] (s) at (0,0)
{
\black
{$
\mathbf
{
s
}$}}
;
\node
[draw,ublue,fill=blue!10,thick,anchor=center,circle,inner sep=3.3pt] (t) at ([xshift=1.5in]s.east)
{
\black
{$
\mathbf
{
t
}$}}
;
\draw
[<->,thick,] (s.east) -- (t.west) node [pos=0.5,draw,fill=white]
{
噪声信道
}
;
\draw
[->,thick,] (s.east) -- (t.west) node [pos=0.5,draw,fill=white]
{
噪声信道
}
;
\draw
[->,thick]
(s.east) -- ([xshift=2.2em]s.east);
\node
[anchor=east] at (s.west)
{
\scriptsize
{
信宿
}}
;
\node
[anchor=west] at (t.east)
{
\scriptsize
{
信源
}}
;
...
...
Book/Chapter3/Figures/figure-process-of-machine-translation.tex
查看文件 @
bd87e7ad
...
...
@@ -5,7 +5,7 @@
\node
[anchor=west] (s1) at (0,0)
{{
我
}}
;
\node
[anchor=west] (s2) at ([xshift=2em]s1.east)
{{
对
}}
;
\node
[anchor=west] (s3) at ([xshift=2em]s2.east)
{{
你
}}
;
\node
[anchor=west] (s4) at ([xshift=2em]s3.east)
{{
表示
}}
;
\node
[anchor=west] (s4) at ([xshift=2em]s3.east)
{{
感到
}}
;
\node
[anchor=west] (s5) at ([xshift=2em]s4.east)
{{
满意
}}
;
\node
[anchor=south west] (sentlabel) at ([yshift=-0.5em]s1.north west)
{
\scriptsize
{{
\color
{
red
}{
待翻译句子(已经分词):
}}}}
;
...
...
@@ -35,7 +35,7 @@
\node
[anchor=north west,inner sep=1pt,fill=black] (tl31) at (t31.north west)
{
\tiny
{{
\color
{
white
}
\textbf
{
3
}}}}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3em] (t41) at ([yshift=-1em]s4.south)
{$
\phi
$}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3em] (t42) at ([yshift=-0.2em]t41.south)
{
show
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3em] (t42) at ([yshift=-0.2em]t41.south)
{
feel
}
;
\node
[anchor=north west,inner sep=1pt,fill=black] (tl41) at (t41.north west)
{
\tiny
{{
\color
{
white
}
\textbf
{
4
}}}}
;
\node
[anchor=north west,inner sep=1pt,fill=black] (tl42) at (t42.north west)
{
\tiny
{{
\color
{
white
}
\textbf
{
4
}}}}
;
...
...
Book/Chapter3/Figures/greedy-mt-decoding-process-1.tex
查看文件 @
bd87e7ad
...
...
@@ -16,7 +16,7 @@
\node
[anchor=west] (s1) at (0,0)
{{
我
}}
;
\node
[anchor=west] (s2) at ([xshift=3em]s1.east)
{{
对
}}
;
\node
[anchor=west] (s3) at ([xshift=3em]s2.east)
{{
你
}}
;
\node
[anchor=west] (s4) at ([xshift=2.5em]s3.east)
{{
表示
}}
;
\node
[anchor=west] (s4) at ([xshift=2.5em]s3.east)
{{
感到
}}
;
\node
[anchor=west] (s5) at ([xshift=2.5em]s4.east)
{{
满意
}}
;
\node
[anchor=south west,inner sep=1pt] (sentlabel) at ([yshift=0.3em]s1.north west)
{
\scriptsize
{{
输入: 待翻译句子(已经分词)
}}}
;
...
...
@@ -53,8 +53,8 @@
{
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t41) at ([yshift=-1.3em]s4.south)
{$
\phi
$}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t42) at ([yshift=-0.2em]t41.south)
{
show
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t43) at ([yshift=-0.2em]t42.south)
{
show
s
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t42) at ([yshift=-0.2em]t41.south)
{
feel
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t43) at ([yshift=-0.2em]t42.south)
{
feel
s
}
;
}
{
...
...
@@ -121,7 +121,7 @@
\node
[anchor=west] (s1) at (0,0)
{{
我
}}
;
\node
[anchor=west] (s2) at ([xshift=3em]s1.east)
{{
对
}}
;
\node
[anchor=west] (s3) at ([xshift=3em]s2.east)
{{
你
}}
;
\node
[anchor=west] (s4) at ([xshift=2.5em]s3.east)
{{
表示
}}
;
\node
[anchor=west] (s4) at ([xshift=2.5em]s3.east)
{{
感到
}}
;
\node
[anchor=west] (s5) at ([xshift=2.5em]s4.east)
{{
满意
}}
;
\node
[anchor=south west,inner sep=1pt] (sentlabel) at ([yshift=0.3em]s1.north west)
{
\scriptsize
{{
输入: 待翻译句子(已经分词)
}}}
;
...
...
@@ -160,8 +160,8 @@
{
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t41) at ([yshift=-1.3em]s4.south)
{$
\phi
$}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t42) at ([yshift=-0.2em]t41.south)
{
show
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t43) at ([yshift=-0.2em]t42.south)
{
show
s
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t42) at ([yshift=-0.2em]t41.south)
{
feel
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t43) at ([yshift=-0.2em]t42.south)
{
feel
s
}
;
}
...
...
Book/Chapter3/Figures/greedy-mt-decoding-process-3.tex
查看文件 @
bd87e7ad
...
...
@@ -11,7 +11,7 @@
\node
[anchor=west] (s1) at (0,0)
{{
我
}}
;
\node
[anchor=west] (s2) at ([xshift=3em]s1.east)
{{
对
}}
;
\node
[anchor=west] (s3) at ([xshift=3em]s2.east)
{{
你
}}
;
\node
[anchor=west] (s4) at ([xshift=2.5em]s3.east)
{{
表示
}}
;
\node
[anchor=west] (s4) at ([xshift=2.5em]s3.east)
{{
感到
}}
;
\node
[anchor=west] (s5) at ([xshift=2.5em]s4.east)
{{
满意
}}
;
\node
[anchor=south west,inner sep=1pt] (sentlabel) at ([yshift=0.3em]s1.north west)
{
\scriptsize
{{
输入: 待翻译句子(已经分词)
}}}
;
...
...
@@ -50,8 +50,8 @@
{
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t41) at ([yshift=-1.3em]s4.south)
{$
\phi
$}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t42) at ([yshift=-0.2em]t41.south)
{
show
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t43) at ([yshift=-0.2em]t42.south)
{
show
s
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t42) at ([yshift=-0.2em]t41.south)
{
feel
}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t43) at ([yshift=-0.2em]t42.south)
{
feel
s
}
;
}
...
...
@@ -176,7 +176,7 @@
\node
[anchor=west] (s1) at (0,0)
{{
我
}}
;
\node
[anchor=west] (s2) at ([xshift=3em]s1.east)
{{
对
}}
;
\node
[anchor=west] (s3) at ([xshift=3em]s2.east)
{{
你
}}
;
\node
[anchor=west] (s4) at ([xshift=2.5em]s3.east)
{{
表示
}}
;
\node
[anchor=west] (s4) at ([xshift=2.5em]s3.east)
{{
感到
}}
;
\node
[anchor=west] (s5) at ([xshift=2.5em]s4.east)
{{
满意
}}
;
\node
[anchor=south west,inner sep=1pt] (sentlabel) at ([yshift=0.3em]s1.north west)
{
\scriptsize
{{
输入: 待翻译句子(已经分词)
}}}
;
...
...
@@ -215,8 +215,8 @@
{
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (t41) at ([yshift=-1.3em]s4.south)
{$
\phi
$}
;
\node
[anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3.5em] (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] (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] (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)
{
feel
s
}
;
}
...
...
Book/Chapter3/chapter3.tex
查看文件 @
bd87e7ad
...
...
@@ -111,7 +111,7 @@
%----------------------------------------------
\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}
\vspace
{
0.5em
}
...
...
@@ -243,9 +243,9 @@
\begin{example}
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
}
\end{example}
...
...
@@ -253,14 +253,14 @@
\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
\\
&
=
&
\frac
{
4
}{
|
\mathbf
{
s
}
|
\times
|
\mathbf
{
t
}
|
}
\nonumber
\\
&
=
&
\frac
{
4
}{
63
}
&
=
&
\frac
{
4
}{
121
}
\label
{
eq:3-2
}
\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：
\begin{eqnarray}
\textrm
{
P
}
(
\text
{
``机器''
}
,
\text
{
``translation''
}
;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\frac
{
2
}{
63
}
\\
\textrm
{
P
}
(
\text
{
``机器''
}
,
\text
{
``look''
}
;
\mathbf
{
s
}
,
\mathbf
{
t
}
)
&
=
&
\frac
{
0
}{
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
}{
121
}
\label
{
eq:3-3
}
\end{eqnarray}
...
...
@@ -283,13 +283,13 @@
\begin{example}
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
}
\end{example}
...
...
@@ -298,8 +298,8 @@
\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
\\
&
=
&
\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
{
5
}{
98
}
&
=
&
\frac
{
4 + 1
}{
11
\times
11
+ 5
\times
7
}
\nonumber
\\
&
=
&
\frac
{
5
}{
156
}
\label
{
eq:3-5
}
\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
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[step=1,help lines,color=black]
(0,0) grid (4,4);
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0.01
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2
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M
-
1
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\lambda
_
M
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{}
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;
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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)
{}
;
\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 @@
{
\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]
(y2) at (
-5.3em,1.5
)
{
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
$}
;
\node
[anchor=north]
(x1) at (
1em,-3em
)
{$
\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
}$}
;
...
...
@@ -44,4 +44,45 @@
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r15) at (4,4)
{}
;
}
\end{scope}
\begin{scope}
[scale=0.62,xshift=3in]
{
\tiny
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[step=1,help lines,color=black]
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[anchor=north]
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{
0.01
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{
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{
0.02
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\vdots
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{
1.00
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;
\node
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{$
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1
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\lambda
_{
M
-
1
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{$
\lambda
_
M
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{}
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[-,very thick,blue!50] (0,1) -- (1,2);
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[-,very thick,blue!50] (3,2) -- (4,4);
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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}
\ 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
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[anchor=north]
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{
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{$
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[anchor=north]
(y5) at ([xshift=0em,yshift=2em]y4.north)
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{$
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_
2
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...
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\lambda
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M
-
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{
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);
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{$
w
_
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.
00
$}
;
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\end{scope}
\end{tikzpicture}
\ No newline at end of file
Book/Chapter4/Figures/search-space-representation-of-feature-weight
-3
.tex
→
Book/Chapter4/Figures/search-space-representation-of-feature-weight.tex
查看文件 @
bd87e7ad
...
...
@@ -3,13 +3,80 @@
{
\tiny
\draw
[step=1,help lines,color=black]
grid (4,4);
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}
;
\node
[anchor=north]
(y1) at ([xshift=0em,yshift=-3.3em]y2.south)
{
0.00
}
;
\node
[anchor=north]
(y3) at ([xshift=0em,yshift=4.5em]y2.north)
{
0.02
}
;
\node
[anchor=north]
(y4) at ([xshift=0em,yshift=6.6em]y3.north)
{$
\vdots
$}
;
\node
[anchor=north]
(y5) at ([xshift=0em,yshift=2em]y4.north)
{
1.00
}
;
\node
[anchor=north]
(x1) at (1em,-3em)
{$
\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
$}
;
\draw
[-](n1) (0,4) -- (0,4.4);
\draw
[-](n2) (1,4) -- (1,4.4);
\draw
[-](n3) (2,4) -- (2,4.4);
\draw
[-](n4) (3,4) -- (3,4.4);
\draw
[-](n5) (4,4) -- (4,4.4);
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r11) at (0,1)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r12) at (1,2)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r13) at (2,1)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r14) at (3,2)
{}
;
\node
[anchor=center,draw,circle,inner sep=1.5pt,blue!30,fill=blue!30] (r15) at (4,4)
{}
;
\draw
[-,very thick,blue!50] (0,1) -- (1,2) -- (2,1) -- (3,2) -- (4,4);
\node
[anchor=north]
(p1) at (5.7,4.3)
{
\scriptsize
{$
\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
$}
;
}
\end{scope}
\begin{scope}
[scale=0.55,xshift=6.8in]
{
\tiny
\draw
[step=1,help lines,color=black]
grid (4,4);
\node
[anchor=north]
(y2) at (-5.3em,1.5)
{
0.01
}
;
\node
[anchor=north]
(y1) at ([xshift=0em,yshift=-3.3em]y2.south)
{
0.00
}
;
\node
[anchor=north]
(y3) at ([xshift=0em,yshift=4.5em]y2.north)
{
0.02
}
;
\node
[anchor=north]
(y4) at ([xshift=0em,yshift=6.6em]y3.north)
{$
\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
$}
;
\node
[anchor=north]
(x1) at (
1em,-3em
)
{$
\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
}$}
;
...
...
Book/Chapter4/chapter4.tex
查看文件 @
bd87e7ad
...
...
@@ -701,9 +701,7 @@ dr = start_i-end_{i-1}-1
%----------------------------------------------
\begin{figure}
[htp]
\centering
\begin{tabular}
{
l l l
}
&
\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}
\input
{
./Chapter4/Figures/search-space-representation-of-feature-weight
}
\caption
{
特征权重的搜索空间表示
}
\label
{
fig:4-23
}
\end{figure}
...
...
@@ -716,9 +714,7 @@ dr = start_i-end_{i-1}-1
%----------------------------------------------
\begin{figure}
[htp]
\centering
\begin{tabular}
{
l l
}
\subfigure
{
\input
{
./Chapter4/Figures/grid-search-1
}}
&
\subfigure
{
\input
{
./Chapter4/Figures/grid-search-2
}}
\\
\end{tabular}
\input
{
./Chapter4/Figures/grid-search
}
\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
}
\end{figure}
...
...
Book/ChapterAppend/chapterappend.tex
查看文件 @
bd87e7ad
...
...
@@ -173,7 +173,7 @@
%----------------------------------------------------------------------------------------
\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}
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
\\
...
...
@@ -181,7 +181,7 @@ h(t,d,n,p, \lambda,\mu, \nu, \zeta) & = & \textrm{P}_{\theta}(\mathbf{s}|\mathb
\label
{
eq:1.1
}
\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}
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
}
\\
...
...
@@ -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
}
\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}
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
}
\end{eqnarray}
...
...
@@ -222,7 +222,7 @@ S = N(b^{\infty}(V(\mathbf{s}|\mathbf{t};2))) \cup (\mathop{\cup}\limits_{ij} N(
\end{itemize}
\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}
\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(
\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}
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
}
\end{eqnarray}
...
...
@@ -255,7 +255,7 @@ d_1(\Delta_j|ca,cb;\mathbf{s},\mathbf{t}) = \mu_{1cacb}^{-1} \times \sum_{k=1}^{
\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
}
\\
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
}
\end{eqnarray}
...
...
@@ -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：
\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
}
\end{eqnarray}
...
...
@@ -280,7 +280,7 @@ d_{>1}(\Delta_j|cb;\mathbf{s},\mathbf{t}) = \mu_{>1cb}^{-1} \times \sum_{k=1}^{K
\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
}
\\
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
}
\end{eqnarray}
...
...
@@ -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
}
\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
%----------------------------------------------------------------------------------------
...
...
@@ -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
}
\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}
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
}
\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
\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
}
\\
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
\\
&
&
\cdot
\delta
(v
_
m-
\phi
_
i+1,v
_
y)
\cdot
\delta
(v
_{
\pi
_{
i1
}}
,v
_{
\pi
_{
i1
}
-1
}
)
\Big
]
\label
{
eq:1.25
}
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-
\
var
phi
_
i+1,v
_
y)
\cdot
\delta
(v
_{
\pi
_{
i1
}}
,v
_{
\pi
_{
i1
}
-1
}
)
\Big
]
\label
{
eq:1.25
}
\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：
\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
}
\end{eqnarray}
...
...
@@ -326,18 +326,18 @@ d_{>1}(\Delta_j|cb,v;\mathbf{s},\mathbf{t}) = \mu_{>1cb}^{-1} \times \sum_{k=1}^
\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
}
\\
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
\\
&
&
\cdot
\delta
(B(
\tau
_{
[i]k
}
) ,cb)
\cdot
\delta
(v
_
m-v
_{
\pi
_{
i(k-1)
}}
-
\phi
_
i+k,v)
\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)
}}
-
\
var
phi
_
i+k,v)
\nonumber
\\
&
&
\cdot
\delta
(v
_{
\pi
_{
i1
}}
,v
_{
\pi
_{
i1
}
-1
}
)
\big
]
\Big
]
\label
{
eq:1.28
}
\end{eqnarray}
\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
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
\begin{eqnarray}
...
...
@@ -346,7 +346,7 @@ s_{>1}(\Delta_j|cb,v;\mathbf{a},\mathbf{s},\mathbf{t}) & = & \sum_{i=1}^l\Big[\v
\end{eqnarray}
\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}
...
...
Book/mt-book-xelatex.idx
查看文件 @
bd87e7ad
...
...
@@ -7,14 +7,14 @@
\indexentry{数据驱动|hyperpage}{23}
\indexentry{Data-Driven|hyperpage}{23}
\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{Quality Evaluation|hyperpage}{32}
\indexentry{无参考答案的评价|hyperpage}{32}
\indexentry{Quality Estimation|hyperpage}{32}
\indexentry{$n$元语法单元|hyperpage}{33}
\indexentry{$n$-gram准确率|hyperpage}{3
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...
...
@@ -115,10 +115,10 @@
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...
...
@@ -153,525 +153,525 @@
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8
}
\indexentry{Recurrent Neural Network|hyperpage}{26
8
}
\indexentry{循环神经网络语言模型|hyperpage}{26
8
}
\indexentry{RNNLM|hyperpage}{26
8
}
\indexentry{循环单元|hyperpage}{26
8
}
\indexentry{RNN Cell|hyperpage}{26
8
}
\indexentry{自注意力机制|hyperpage}{2
70
}
\indexentry{Self-Attention Mechanism|hyperpage}{2
70
}
\indexentry{注意力权重|hyperpage}{2
70
}
\indexentry{Attention Weight|hyperpage}{2
70
}
\indexentry{困惑度|hyperpage}{2
71
}
\indexentry{Perplexity|hyperpage}{2
71
}
\indexentry{One-hot编码|hyperpage}{2
71
}
\indexentry{独热编码|hyperpage}{2
71
}
\indexentry{分布式表示|hyperpage}{2
72
}
\indexentry{Distributed Representation|hyperpage}{2
72
}
\indexentry{词嵌入|hyperpage}{2
72
}
\indexentry{Word Embedding|hyperpage}{2
72
}
\indexentry{句子表示模型|hyperpage}{27
4
}
\indexentry{句子的表示|hyperpage}{27
4
}
\indexentry{表示学习|hyperpage}{27
4
}
\indexentry{Representation Learning|hyperpage}{27
4
}
\indexentry{可解释机器学习|hyperpage}{27
8
}
\indexentry{Explainable Machine Learning|hyperpage}{27
8
}
\indexentry{神经机器翻译|hyperpage}{2
81
}
\indexentry{Neural Machine Translation|hyperpage}{2
81
}
\indexentry{分布式表示|hyperpage}{2
83
}
\indexentry{Distributed Representation|hyperpage}{2
83
}
\indexentry{特征工程|hyperpage}{28
9
}
\indexentry{Feature Engineering|hyperpage}{28
9
}
\indexentry{编码器-解码器模型|hyperpage}{2
90
}
\indexentry{Encoder-Decoder Paradigm|hyperpage}{2
90
}
\indexentry{编码器-解码器框架|hyperpage}{2
90
}
\indexentry{循环神经网络|hyperpage}{2
95
}
\indexentry{Recurrent Neural Network, RNN|hyperpage}{2
95
}
\indexentry{词嵌入|hyperpage}{29
7
}
\indexentry{Word Embedding|hyperpage}{29
7
}
\indexentry{表示学习|hyperpage}{29
7
}
\indexentry{Representation Learning|hyperpage}{29
7
}
\indexentry{生成|hyperpage}{29
7
}
\indexentry{Generation|hyperpage}{29
7
}
\indexentry{长短时记忆|hyperpage}{
302
}
\indexentry{Long Short-Term Memory|hyperpage}{
302
}
\indexentry{遗忘|hyperpage}{
302
}
\indexentry{记忆更新|hyperpage}{
303
}
\indexentry{输出|hyperpage}{
303
}
\indexentry{门循环单元|hyperpage}{
304
}
\indexentry{Gated Recurrent Unit，GRU|hyperpage}{
304
}
\indexentry{注意力权重|hyperpage}{30
9
}
\indexentry{Attention Weight|hyperpage}{30
9
}
\indexentry{一阶矩估计|hyperpage}{3
15
}
\indexentry{First Moment Estimation|hyperpage}{3
15
}
\indexentry{二阶矩估计|hyperpage}{3
15
}
\indexentry{Second Moment Estimation|hyperpage}{3
15
}
\indexentry{学习率|hyperpage}{3
16
}
\indexentry{Learning Rate|hyperpage}{3
16
}
\indexentry{逐渐预热|hyperpage}{31
6
}
\indexentry{Gradual Warmup|hyperpage}{31
6
}
\indexentry{分段常数衰减|hyperpage}{31
7
}
\indexentry{Piecewise Constant Decay|hyperpage}{31
7
}
\indexentry{数据并行|hyperpage}{31
8
}
\indexentry{模型并行|hyperpage}{31
8
}
\indexentry{全搜索|hyperpage}{3
20
}
\indexentry{Full Search|hyperpage}{3
20
}
\indexentry{贪婪搜索|hyperpage}{3
20
}
\indexentry{Greedy Search|hyperpage}{3
20
}
\indexentry{束搜索|hyperpage}{3
20
}
\indexentry{Beam Search|hyperpage}{3
20
}
\indexentry{自回归模型|hyperpage}{3
20
}
\indexentry{Autoregressive Model|hyperpage}{3
21
}
\indexentry{非自回归模型|hyperpage}{3
21
}
\indexentry{Non-autoregressive Model|hyperpage}{3
21
}
\indexentry{自注意力机制|hyperpage}{32
6
}
\indexentry{Self-Attention|hyperpage}{32
6
}
\indexentry{特征提取|hyperpage}{32
7
}
\indexentry{自注意力子层|hyperpage}{32
8
}
\indexentry{Self-attention Sub-layer|hyperpage}{32
8
}
\indexentry{前馈神经网络子层|hyperpage}{32
8
}
\indexentry{Feed-forward Sub-layer|hyperpage}{32
8
}
\indexentry{残差连接|hyperpage}{32
8
}
\indexentry{Residual Connection|hyperpage}{32
8
}
\indexentry{层正则化|hyperpage}{32
8
}
\indexentry{Layer Normalization|hyperpage}{32
8
}
\indexentry{编码-解码注意力子层|hyperpage}{32
9
}
\indexentry{Encoder-decoder Attention Sub-layer|hyperpage}{32
9
}
\indexentry{词嵌入|hyperpage}{32
9
}
\indexentry{Word Embedding|hyperpage}{32
9
}
\indexentry{位置编码|hyperpage}{32
9
}
\indexentry{Position Embedding|hyperpage}{32
9
}
\indexentry{点乘注意力|hyperpage}{3
33
}
\indexentry{Scaled Dot-Product Attention|hyperpage}{3
33
}
\indexentry{多头注意力|hyperpage}{3
35
}
\indexentry{Multi-head Attention|hyperpage}{3
35
}
\indexentry{残差连接|hyperpage}{3
36
}
\indexentry{短连接|hyperpage}{33
7
}
\indexentry{Short-cut Connection|hyperpage}{33
7
}
\indexentry{后正则化|hyperpage}{33
8
}
\indexentry{Post-norm|hyperpage}{33
8
}
\indexentry{前正则化|hyperpage}{33
8
}
\indexentry{Pre-norm|hyperpage}{33
8
}
\indexentry{交叉熵损失|hyperpage}{33
9
}
\indexentry{Cross Entropy Loss|hyperpage}{33
9
}
\indexentry{预热|hyperpage}{33
9
}
\indexentry{Warmup|hyperpage}{33
9
}
\indexentry{小批量训练|hyperpage}{3
40
}
\indexentry{Mini-batch Training|hyperpage}{3
40
}
\indexentry{Dropout|hyperpage}{3
40
}
\indexentry{过拟合|hyperpage}{3
40
}
\indexentry{Over fitting|hyperpage}{3
40
}
\indexentry{标签平滑|hyperpage}{3
40
}
\indexentry{Label Smoothing|hyperpage}{3
40
}
\indexentry{序列到序列的转换/生成问题|hyperpage}{3
42
}
\indexentry{Sequence-to-Sequence Problem|hyperpage}{3
42
}
\indexentry{未登录词|hyperpage}{3
53
}
\indexentry{Out of Vocabulary Word，OOV Word|hyperpage}{3
53
}
\indexentry{子词切分|hyperpage}{3
53
}
\indexentry{Sub-word Segmentation|hyperpage}{3
53
}
\indexentry{标准化|hyperpage}{3
53
}
\indexentry{Normalization|hyperpage}{3
53
}
\indexentry{数据清洗|hyperpage}{3
53
}
\indexentry{Dada Cleaning|hyperpage}{3
53
}
\indexentry{数据选择|hyperpage}{3
55
}
\indexentry{Data Selection|hyperpage}{3
55
}
\indexentry{数据过滤|hyperpage}{3
55
}
\indexentry{Data Filtering|hyperpage}{3
55
}
\indexentry{开放词表|hyperpage}{35
8
}
\indexentry{Open-Vocabulary|hyperpage}{35
8
}
\indexentry{子词|hyperpage}{35
9
}
\indexentry{Sub-word|hyperpage}{35
9
}
\indexentry{字节对编码|hyperpage}{35
9
}
\indexentry{双字节编码|hyperpage}{35
9
}
\indexentry{Byte Pair Encoding，BPE|hyperpage}{35
9
}
\indexentry{正则化|hyperpage}{3
62
}
\indexentry{Regularization|hyperpage}{3
62
}
\indexentry{过拟合问题|hyperpage}{3
62
}
\indexentry{Overfitting Problem|hyperpage}{3
62
}
\indexentry{反问题|hyperpage}{3
62
}
\indexentry{Inverse Problem|hyperpage}{3
62
}
\indexentry{适定的|hyperpage}{3
63
}
\indexentry{Well-posed|hyperpage}{3
63
}
\indexentry{不适定问题|hyperpage}{3
63
}
\indexentry{Ill-posed Problem|hyperpage}{3
63
}
\indexentry{降噪|hyperpage}{3
63
}
\indexentry{Denoising|hyperpage}{3
63
}
\indexentry{泛化|hyperpage}{3
64
}
\indexentry{Generalization|hyperpage}{3
64
}
\indexentry{标签平滑|hyperpage}{3
65
}
\indexentry{Label Smoothing|hyperpage}{3
65
}
\indexentry{相互适应|hyperpage}{3
66
}
\indexentry{Co-Adaptation|hyperpage}{3
66
}
\indexentry{集成学习|hyperpage}{3
68
}
\indexentry{Ensemble Learning|hyperpage}{3
68
}
\indexentry{容量|hyperpage}{36
9
}
\indexentry{Capacity|hyperpage}{36
9
}
\indexentry{宽残差网络|hyperpage}{36
9
}
\indexentry{Wide Residual Network|hyperpage}{36
9
}
\indexentry{探测任务|hyperpage}{3
71
}
\indexentry{Probing Task|hyperpage}{3
71
}
\indexentry{表面信息|hyperpage}{3
71
}
\indexentry{Surface Information|hyperpage}{3
71
}
\indexentry{语法信息|hyperpage}{3
71
}
\indexentry{Syntactic Information|hyperpage}{3
71
}
\indexentry{语义信息|hyperpage}{3
71
}
\indexentry{Semantic Information|hyperpage}{3
71
}
\indexentry{词嵌入|hyperpage}{3
71
}
\indexentry{Embedding|hyperpage}{3
71
}
\indexentry{数据并行|hyperpage}{3
72
}
\indexentry{Data Parallelism|hyperpage}{3
72
}
\indexentry{模型并行|hyperpage}{3
72
}
\indexentry{Model Parallelism|hyperpage}{3
72
}
\indexentry{小批量训练|hyperpage}{3
72
}
\indexentry{Mini-batch Training|hyperpage}{3
72
}
\indexentry{课程学习|hyperpage}{3
74
}
\indexentry{Curriculum Learning|hyperpage}{3
74
}
\indexentry{推断|hyperpage}{3
75
}
\indexentry{Inference|hyperpage}{3
75
}
\indexentry{解码|hyperpage}{3
75
}
\indexentry{Decoding|hyperpage}{3
75
}
\indexentry{准确性|hyperpage}{3
75
}
\indexentry{Accuracy|hyperpage}{3
75
}
\indexentry{时延|hyperpage}{3
75
}
\indexentry{Latency|hyperpage}{3
75
}
\indexentry{时延|hyperpage}{3
75
}
\indexentry{Memory|hyperpage}{3
75
}
\indexentry{搜索错误|hyperpage}{3
75
}
\indexentry{Search Error|hyperpage}{3
75
}
\indexentry{模型错误|hyperpage}{3
75
}
\indexentry{Modeling Error|hyperpage}{3
75
}
\indexentry{重排序|hyperpage}{3
77
}
\indexentry{Re-ranking|hyperpage}{3
77
}
\indexentry{双向推断|hyperpage}{3
77
}
\indexentry{Bidirectional Inference|hyperpage}{3
77
}
\indexentry{批量推断|hyperpage}{3
8
1}
\indexentry{Batch Inference|hyperpage}{3
8
1}
\indexentry{批量处理|hyperpage}{3
8
1}
\indexentry{Batching|hyperpage}{3
8
1}
\indexentry{二值网络|hyperpage}{3
8
3}
\indexentry{Binarized Neural Networks|hyperpage}{3
8
3}
\indexentry{自回归翻译|hyperpage}{3
8
3}
\indexentry{Autoregressive Translation|hyperpage}{3
8
3}
\indexentry{非自回归翻译|hyperpage}{3
83
}
\indexentry{
Regressive Translation|hyperpage}{383
}
\indexentry{繁衍率|hyperpage}{3
83
}
\indexentry{Fertility|hyperpage}{3
83
}
\indexentry{偏置|hyperpage}{3
8
5}
\indexentry{Bias|hyperpage}{3
8
5}
\indexentry{退化|hyperpage}{3
8
5}
\indexentry{Degenerate|hyperpage}{3
8
5}
\indexentry{过翻译|hyperpage}{3
86
}
\indexentry{Over Translation|hyperpage}{3
86
}
\indexentry{欠翻译|hyperpage}{3
86
}
\indexentry{Under Translation|hyperpage}{3
86
}
\indexentry{充分性|hyperpage}{3
8
7}
\indexentry{Adequacy|hyperpage}{3
8
7}
\indexentry{系统融合|hyperpage}{3
8
8}
\indexentry{System Combination|hyperpage}{3
8
8}
\indexentry{假设选择|hyperpage}{3
88
}
\indexentry{Hypothesis Selection|hyperpage}{3
88
}
\indexentry{多样性|hyperpage}{3
88
}
\indexentry{Diversity|hyperpage}{3
88
}
\indexentry{重排序|hyperpage}{3
8
9}
\indexentry{Re-ranking|hyperpage}{3
8
9}
\indexentry{混淆网络|hyperpage}{3
90
}
\indexentry{Confusion Network|hyperpage}{3
90
}
\indexentry{动态线性层聚合方法|hyperpage}{3
94
}
\indexentry{Dynamic Linear Combination of Layers，DLCL|hyperpage}{3
94
}
\indexentry{相互适应|hyperpage}{3
98
}
\indexentry{Co-adaptation|hyperpage}{3
98
}
\indexentry{数据增强|hyperpage}{
40
1}
\indexentry{Data Augmentation|hyperpage}{
40
1}
\indexentry{回译|hyperpage}{
40
1}
\indexentry{Back Translation|hyperpage}{
40
1}
\indexentry{迭代式回译|hyperpage}{
401
}
\indexentry{Iterative Back Translation|hyperpage}{
401
}
\indexentry{前向翻译|hyperpage}{
40
2}
\indexentry{Forward Translation|hyperpage}{
40
2}
\indexentry{预训练|hyperpage}{
402
}
\indexentry{Pre-training|hyperpage}{
402
}
\indexentry{微调|hyperpage}{
402
}
\indexentry{Fine-tuning|hyperpage}{
402
}
\indexentry{多任务学习|hyperpage}{
40
4}
\indexentry{Multitask Learning|hyperpage}{
40
4}
\indexentry{模型压缩|hyperpage}{
405
}
\indexentry{Model Compression|hyperpage}{
405
}
\indexentry{学习难度|hyperpage}{
405
}
\indexentry{Learning Difficulty|hyperpage}{
40
6}
\indexentry{教师模型|hyperpage}{
40
6}
\indexentry{Teacher Model|hyperpage}{
40
6}
\indexentry{学生模型|hyperpage}{
406
}
\indexentry{Student Model|hyperpage}{
406
}
\indexentry{基于单词的知识精炼|hyperpage}{
406
}
\indexentry{Word-level Knowledge Distillation|hyperpage}{
406
}
\indexentry{基于序列的知识精炼|hyperpage}{
40
7}
\indexentry{Sequence-level Knowledge Distillation|hyperpage}{
40
7}
\indexentry{中间层输出|hyperpage}{
40
8}
\indexentry{Hint-based Knowledge Transfer|hyperpage}{
40
8}
\indexentry{注意力分布|hyperpage}{
40
8}
\indexentry{Attention To Attention Transfer|hyperpage}{
40
8}
\indexentry{循环一致性|hyperpage}{4
10
}
\indexentry{Circle Consistency|hyperpage}{4
10
}
\indexentry{翻译中回译|hyperpage}{4
11
}
\indexentry{On-the-fly Back-translation|hyperpage}{4
11
}
\indexentry{网络结构搜索技术|hyperpage}{4
1
4}
\indexentry{Neural Architecture Search；NAS|hyperpage}{4
1
4}
\indexentry{树到串翻译规则|hyperpage}{1
79
}
\indexentry{Tree-to-String Translation Rule|hyperpage}{1
79
}
\indexentry{树到树翻译规则|hyperpage}{1
79
}
\indexentry{Tree-to-Tree Translation Rule|hyperpage}{1
79
}
\indexentry{树片段|hyperpage}{18
0
}
\indexentry{Tree Fragment|hyperpage}{18
0
}
\indexentry{同步树替换文法规则|hyperpage}{18
1
}
\indexentry{Synchronous Tree Substitution Grammar Rule|hyperpage}{18
1
}
\indexentry{边缘集合|hyperpage}{18
7
}
\indexentry{Frontier Set|hyperpage}{18
7
}
\indexentry{最小规则|hyperpage}{1
88
}
\indexentry{Minimal Rules|hyperpage}{1
88
}
\indexentry{二叉化|hyperpage}{19
1
}
\indexentry{Binarization|hyperpage}{19
1
}
\indexentry{基于短语的特征|hyperpage}{19
5
}
\indexentry{基于句法的特征|hyperpage}{19
5
}
\indexentry{有向超图|hyperpage}{19
6
}
\indexentry{Directed Hyper-graph|hyperpage}{19
6
}
\indexentry{超边|hyperpage}{19
6
}
\indexentry{Hyper-edge|hyperpage}{19
6
}
\indexentry{半环分析|hyperpage}{
197
}
\indexentry{Semi-ring Parsing|hyperpage}{
197
}
\indexentry{组合|hyperpage}{
198
}
\indexentry{Composition|hyperpage}{
198
}
\indexentry{基于串的解码|hyperpage}{
199
}
\indexentry{String-based Decoding|hyperpage}{
199
}
\indexentry{基于树的解码|hyperpage}{
199
}
\indexentry{Tree-based Decoding|hyperpage}{
199
}
\indexentry{Lexicalized Norm Form|hyperpage}{20
2
}
\indexentry{人工神经网络|hyperpage}{2
07
}
\indexentry{Artificial Neural Networks|hyperpage}{2
07
}
\indexentry{神经网络|hyperpage}{2
07
}
\indexentry{Neural Networks|hyperpage}{2
07
}
\indexentry{深度学习|hyperpage}{2
08
}
\indexentry{Deep Learning|hyperpage}{2
08
}
\indexentry{连接主义|hyperpage}{2
09
}
\indexentry{Connectionism|hyperpage}{2
09
}
\indexentry{分布式表示|hyperpage}{2
09
}
\indexentry{Distributed representation|hyperpage}{2
09
}
\indexentry{符号主义|hyperpage}{2
09
}
\indexentry{Symbolicism|hyperpage}{2
09
}
\indexentry{端到端学习|hyperpage}{21
1
}
\indexentry{End-to-End Learning|hyperpage}{21
1
}
\indexentry{表示学习|hyperpage}{21
1
}
\indexentry{Representation Learning|hyperpage}{21
1
}
\indexentry{分布式表示|hyperpage}{21
2
}
\indexentry{Distributed Representation|hyperpage}{21
2
}
\indexentry{标量|hyperpage}{21
3
}
\indexentry{Scalar|hyperpage}{21
3
}
\indexentry{向量|hyperpage}{21
3
}
\indexentry{Vector|hyperpage}{21
3
}
\indexentry{矩阵|hyperpage}{21
3
}
\indexentry{Matrix|hyperpage}{21
3
}
\indexentry{转置|hyperpage}{21
4
}
\indexentry{Transpose|hyperpage}{21
4
}
\indexentry{按元素加法|hyperpage}{21
4
}
\indexentry{Element-wise Addition|hyperpage}{21
4
}
\indexentry{数乘|hyperpage}{21
5
}
\indexentry{Scalar Multiplication|hyperpage}{21
5
}
\indexentry{按元素乘积|hyperpage}{2
16
}
\indexentry{Element-wise Product|hyperpage}{2
16
}
\indexentry{线性映射|hyperpage}{2
16
}
\indexentry{Linear Mapping|hyperpage}{2
16
}
\indexentry{线性变换|hyperpage}{2
16
}
\indexentry{Linear Transformation|hyperpage}{2
16
}
\indexentry{范数|hyperpage}{2
17
}
\indexentry{Norm|hyperpage}{2
17
}
\indexentry{欧几里得范数|hyperpage}{2
18
}
\indexentry{Euclidean Norm|hyperpage}{2
18
}
\indexentry{Frobenius 范数|hyperpage}{2
18
}
\indexentry{Frobenius Norm|hyperpage}{2
18
}
\indexentry{权重|hyperpage}{2
19
}
\indexentry{weight|hyperpage}{2
19
}
\indexentry{张量|hyperpage}{2
29
}
\indexentry{Tensor|hyperpage}{2
29
}
\indexentry{阶|hyperpage}{2
29
}
\indexentry{Rank|hyperpage}{2
29
}
\indexentry{广播机制|hyperpage}{23
3
}
\indexentry{向量化|hyperpage}{23
3
}
\indexentry{Vectorization|hyperpage}{23
3
}
\indexentry{前向传播|hyperpage}{2
37
}
\indexentry{计算图|hyperpage}{2
38
}
\indexentry{Computation Graph|hyperpage}{2
38
}
\indexentry{模型参数|hyperpage}{2
39
}
\indexentry{Model Parameters|hyperpage}{2
39
}
\indexentry{训练|hyperpage}{2
39
}
\indexentry{Training|hyperpage}{2
39
}
\indexentry{有标注数据|hyperpage}{2
39
}
\indexentry{Annotated Data/Labeled Data|hyperpage}{2
39
}
\indexentry{有指导的训练|hyperpage}{2
39
}
\indexentry{有监督的训练|hyperpage}{2
39
}
\indexentry{Supervised Training|hyperpage}{2
39
}
\indexentry{训练数据集合|hyperpage}{24
0
}
\indexentry{Training Data Set|hyperpage}{24
0
}
\indexentry{损失函数|hyperpage}{24
0
}
\indexentry{Loss Function|hyperpage}{24
0
}
\indexentry{目标函数|hyperpage}{24
0
}
\indexentry{Objective Function|hyperpage}{24
0
}
\indexentry{代价函数|hyperpage}{24
2
}
\indexentry{Cost Function|hyperpage}{24
2
}
\indexentry{梯度下降方法|hyperpage}{24
2
}
\indexentry{Gradient Descent Method|hyperpage}{24
2
}
\indexentry{参数更新的规则|hyperpage}{24
2
}
\indexentry{Update Rule|hyperpage}{24
2
}
\indexentry{学习率|hyperpage}{24
2
}
\indexentry{Learning Rate|hyperpage}{24
2
}
\indexentry{基于梯度的方法|hyperpage}{24
2
}
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75
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75
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77
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77
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3
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83
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83
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83
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89
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296
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296
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297
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298
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09
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09
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09
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14
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14
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2
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3
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3
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3
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26
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26
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28
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28
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2
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33
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33
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33
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33
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33
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45
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45
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45
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45
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47
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47
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47
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47
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54
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54
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54
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54
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54
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54
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54
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54
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54
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54
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55
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55
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55
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55
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57
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57
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58
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62
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62
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62
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62
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63
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63
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63
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63
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63
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63
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65
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65
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66
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66
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66
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66
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66
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66
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66
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66
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66
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66
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66
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66
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66
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66
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68
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68
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68
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68
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7
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7
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7
1}
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7
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7
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7
3}
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7
3}
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7
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74
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\indexentry{
Non-Autoregressive Translation|hyperpage}{374
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74
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74
}
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7
5}
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7
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7
5}
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7
5}
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77
}
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77
}
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77
}
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77
}
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7
7}
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7
7}
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7
8}
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7
8}
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79
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79
}
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79
}
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79
}
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7
9}
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7
9}
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81
}
\indexentry{Confusion Network|hyperpage}{3
81
}
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85
}
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85
}
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89
}
\indexentry{Co-adaptation|hyperpage}{3
89
}
\indexentry{数据增强|hyperpage}{
39
1}
\indexentry{Data Augmentation|hyperpage}{
39
1}
\indexentry{回译|hyperpage}{
39
1}
\indexentry{Back Translation|hyperpage}{
39
1}
\indexentry{迭代式回译|hyperpage}{
392
}
\indexentry{Iterative Back Translation|hyperpage}{
392
}
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39
2}
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39
2}
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393
}
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393
}
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393
}
\indexentry{Fine-tuning|hyperpage}{
393
}
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39
4}
\indexentry{Multitask Learning|hyperpage}{
39
4}
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396
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396
}
\indexentry{学习难度|hyperpage}{
396
}
\indexentry{Learning Difficulty|hyperpage}{
39
6}
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39
6}
\indexentry{Teacher Model|hyperpage}{
39
6}
\indexentry{学生模型|hyperpage}{
397
}
\indexentry{Student Model|hyperpage}{
397
}
\indexentry{基于单词的知识精炼|hyperpage}{
397
}
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397
}
\indexentry{基于序列的知识精炼|hyperpage}{
39
7}
\indexentry{Sequence-level Knowledge Distillation|hyperpage}{
39
7}
\indexentry{中间层输出|hyperpage}{
39
8}
\indexentry{Hint-based Knowledge Transfer|hyperpage}{
39
8}
\indexentry{注意力分布|hyperpage}{
39
8}
\indexentry{Attention To Attention Transfer|hyperpage}{
39
8}
\indexentry{循环一致性|hyperpage}{4
01
}
\indexentry{Circle Consistency|hyperpage}{4
01
}
\indexentry{翻译中回译|hyperpage}{4
02
}
\indexentry{On-the-fly Back-translation|hyperpage}{4
02
}
\indexentry{网络结构搜索技术|hyperpage}{4
0
4}
\indexentry{Neural Architecture Search；NAS|hyperpage}{4
0
4}
Book/mt-book-xelatex.ptc
查看文件 @
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Section03-Word-Based-Models/section03.tex
查看文件 @
bd87e7ad
...
...
@@ -909,12 +909,12 @@
\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
}
\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}
\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
\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] (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] (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
}}}$}}
;
...
...
@@ -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
\end{itemize}
\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}
\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
}
...
...
@@ -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
\\
&
\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
<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
<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
<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}
}
...
...
@@ -3730,7 +3730,7 @@ $\mathbf{s}$ = 在 桌子 上 \ \ \ \ \ $\mathbf{t}$ = $t_0$ on the table \ \ \
{
\small
\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
\end{eqnarray}
}
...
...
@@ -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
\begin{frame}
{
在整个数据集上计算
}
\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}
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}
\item
<2->
\textbf
{
于是
}
\begin{center}
...
...
@@ -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
)
$}
;
\node
[anchor=west] (eq2) at (eq1.east)
{$
=
$
\
}
;
\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
]
}
)
$}
;
\node
[anchor=north west] (eq4) at (eq2.east)
{$
\sum
_{
s
_
u
}
\sum
_{
i
=
1
}^{
N
}
c
_{
\mathbb
{
E
}}
(
s
_
u|t
_
v;
\mathbf
{
s
}^{
[
i
]
}
,
\mathbf
{
t
}^{
[
i
]
}
)
$}
;
\node
[anchor=south west] (eq3) at ([xshift=1em]eq2.east)
{$
\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
_{
k
=
1
}^{
K
}
c
_{
\mathbb
{
E
}}
(
s
_
u|t
_
v;
\mathbf
{
s
}^{
[
k
]
}
,
\mathbf
{
t
}^{
[
k
]
}
)
$}
;
\visible
<4->
{
\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
\label
{
ibmtraining
}
\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
\\
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
\\
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
{}
\\
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
]
}
)
$
\\
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
}$
\\
10:
\ \textbf
{
return
}
$
f
(
\cdot
|
\cdot
)
$
\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)}{
\end{eqnarray}
\item
\textbf
{
M-Step
}
\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
\\
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
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=
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{enumerate}
\end{frame}
...
...
Section05-Neural-Networks-and-Language-Modeling/section05.tex
查看文件 @
bd87e7ad
...
...
@@ -541,7 +541,7 @@ GPT-2 (Transformer) & Radford et al. & 2019 & \alert{35.7}
\end{itemize}
\item
<2->
\textbf
{
当然
}
，你是一个勇于实践的人
\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
\end{itemize}
\end{itemize}
...
...
@@ -1034,7 +1034,7 @@ T(\alpha \textbf{a}) & = & \alpha T(\textbf{a}) \nonumber
\visible
<3->
{
\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);
\tikzstyle
{
neuron
}
= [rectangle,draw,thick,fill=red!30,red!35,minimum height=2em,minimum width=2em,font=
\small
]
...
...
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