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Toy-MT-Introduction
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单韦乔
Toy-MT-Introduction
Commits
bf0af520
Commit
bf0af520
authored
Nov 08, 2019
by
Lee
Browse files
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Update tables & RNN-MT figures
parent
5518d0ce
隐藏空白字符变更
内嵌
并排
正在显示
1 个修改的文件
包含
54 行增加
和
37 行删除
+54
-37
Section06-Neural-Machine-Translation/section06.tex
+54
-37
没有找到文件。
Section06-Neural-Machine-Translation/section06.tex
查看文件 @
bf0af520
...
...
@@ -69,6 +69,7 @@
%\usetheme{Boadilla}
%\usecolortheme{dolphin}
% not compatible with [scale=?]
\newdimen\XCoord
\newdimen\YCoord
\newdimen\TMP
...
...
@@ -197,44 +198,51 @@
%%% 神经机器翻译的性能增长
\begin{frame}
{
神经机器翻译的进展(续)
}
\begin{itemize}
\item
性能提升
\only
<1>
{
\item
神经机器翻译在大部分场景下已经超越统计机器翻译!
{
\footnotesize
\begin{center}
\begin{tabular}
{
r
}
\setlength
{
\tabcolsep
}{
3pt
}
\renewcommand\arraystretch
{
1
}
\begin{tabular}
{
l
}
\begin{tabular}
{
lcccl
}
\
toprule
\
specialrule
{
1pt
}{
1pt
}{
1pt
}
\multirow
{
2
}{
*
}{
\#
}
&
\multicolumn
{
3
}{
c
}{
自动评价
}
&
\multirow
{
2
}{
*
}{
系统
}
\\
\cline
{
2-4
}
&
\footnotesize
BLEU
&
\footnotesize
HTER
&
\footnotesize
mTER
&
\\
\
midrule
&
BLEU
&
HTER
&
mTER
&
\\
\
specialrule
{
0.6pt
}{
1pt
}{
1pt
}
统计机器翻译
&
25.3
&
28.0
&
21.8
&
PBSY
\\
&
24.6
&
29.9
&
23.4
&
HPB
\\
&
25.8
&
29.0
&
22.7
&
SPB
\\
\
midrule
\
specialrule
{
0.6pt
}{
1pt
}{
1pt
}
神经机器翻译
&
\textbf
{
31.1
}
&
\textbf
{
21.1
}
&
\textbf
{
16.2
}
&
NMT
\\
\bottomrule
\end{tabular}
\\
[4pt]
\specialrule
{
1pt
}{
1pt
}{
1pt
}
\end{tabular}
\\
\addlinespace
[-0.3ex]
\tiny
*Neural versus Phrase-Based Machine Translation Quality: a Case Study
\\
\end{tabular}
\end{center}
}
\item
Human party MT
\only
<2>
{
\item
微软报道在部分场景下机器翻译质量已经超越人类!
{
\footnotesize
\begin{center}
\begin{tabular}
{
r
}
\renewcommand\arraystretch
{
1
}
\begin{tabular}
{
l
}
\begin{tabular}
{
lrl
}
\
toprule
\
specialrule
{
1pt
}{
1pt
}{
1pt
}
\#
&
人工评价
&
系统
\\
\
midrule
\
specialrule
{
0.6pt
}{
1pt
}{
1pt
}
机器翻译
&
\textbf
{
69.9
}
&
COMBO-6
\\
&
69.8
&
COMBO-4
\\
&
\textbf
{
69.9
}
&
COMBO-5
\\
\
midrule
\
specialrule
{
0.6pt
}{
1pt
}{
1pt
}
人工翻译
&
68.6
&
REFERENCE-HT
\\
&
67.6
&
REFERENCE-PE
\\
&
62.1
&
REFERENCE-WMT
\\
\bottomrule
\end{tabular}
\\
[4pt]
\specialrule
{
1pt
}{
1pt
}{
1pt
}
\end{tabular}
\\
\addlinespace
[-0.3ex]
\tiny
*Achieving Human Parity on Automatic Chinese to English News Translation
\\
\end{tabular}
\end{center}
...
...
@@ -672,17 +680,18 @@ NLP问题的隐含结构假设 & 无隐含结构假设,端到端学习 \\
\newlength
{
\base
}
\setlength
{
\base
}{
0.9cm
}
\begin{scope}
\tikzstyle
{
rnnnode
}
= [rounded corners=2pt,minimum width=0.5
\base
,minimum height=0.5
\base
,draw]
\tikzstyle
{
wordnode
}
= [font=
\tiny
]
\tikzstyle
{
rnnnode
}
= [rounded corners=1pt,minimum size=0.5
\base
,draw,inner sep=0pt,outer sep=0pt]
\tikzstyle
{
wordnode
}
= [font=
\tiny
]
% RNN translation model
\begin{scope}
[local bounding box=RNNMT]
% RNN Encoder
\coordinate
(eemb0) at (0,0);
\foreach
\x
[count=
\y
from 0] in
{
1,2,...,10
}
\node
[rnnnode,
fill=green!30!white,anchor=west]
(eemb
\x
) at ([xshift=0.5
\base
]eemb
\y
.east)
{}
;
\node
[rnnnode,
minimum height=0.1\base,fill=green!30!white,anchor=west]
(eemb
\x
) at ([xshift=0.4
\base
]eemb
\y
.east)
{}
;
\foreach
\x
in
{
1,2,...,10
}
\node
[rnnnode,fill=blue!30!white,anchor=south]
(enc
\x
) at ([yshift=0.5
\base
]eemb
\x
.north)
{}
;
\node
[wordnode,left=0.
5
\base of enc1]
(init)
{$
0
$}
;
\node
[wordnode,left=0.
4
\base of enc1]
(init)
{$
0
$}
;
\node
[wordnode,below=0pt of eemb1]
()
{
你
}
;
\node
[wordnode,below=0pt of eemb2]
()
{
知道
}
;
...
...
@@ -697,12 +706,14 @@ NLP问题的隐含结构假设 & 无隐含结构假设,端到端学习 \\
% RNN Decoder
\foreach
\x
in
{
1,2,...,10
}
\node
[rnnnode,
fill=green!30!white,anchor=south]
(demb
\x
) at ([yshift=1.5
\base
]enc
\x
.north)
{}
;
\node
[rnnnode,
minimum height=0.1\base,fill=green!30!white,anchor=south]
(demb
\x
) at ([yshift=2
\base
]enc
\x
.north)
{}
;
\foreach
\x
in
{
1,2,...,10
}
\node
[rnnnode,fill=blue!30!white,anchor=south]
(dec
\x
) at ([yshift=0.5
\base
]demb
\x
.north)
{}
;
\foreach
\x
in
{
1,2,...,10
}
\node
[rnnnode,minimum height=0.1\base,fill=red!30!white,anchor=south]
(softmax
\x
) at ([yshift=0.5
\base
]dec
\x
.north)
{}
;
% Decoder input words
\node
[wordnode,below=0pt of demb1]
(decwordin)
{$
\langle
$
e
os
$
\rangle
$}
;
\node
[wordnode,below=0pt of demb1]
(decwordin)
{$
\langle
$
s
os
$
\rangle
$}
;
\ExtractX
{$
(
demb
2
.south
)
$}
\ExtractY
{$
(
decwordin.base
)
$}
\node
[wordnode,anchor=base]
() at (
\XCoord
,
\YCoord
)
{
Do
}
;
...
...
@@ -732,32 +743,32 @@ NLP问题的隐含结构假设 & 无隐含结构假设,端到端学习 \\
\node
[wordnode,anchor=base]
() at (
\XCoord
,
\YCoord
)
{
Station
}
;
% Decoder output words
\node
[wordnode,above=0
.5\base of dec
1]
(decwordout)
{
Do
}
;
\ExtractX
{$
(
dec
2
.north
)
$}
\node
[wordnode,above=0
pt of softmax
1]
(decwordout)
{
Do
}
;
\ExtractX
{$
(
softmax
2
.north
)
$}
\ExtractY
{$
(
decwordout.base
)
$}
\node
[wordnode,anchor=base]
() at (
\XCoord
,
\YCoord
)
{
you
}
;
\ExtractX
{$
(
dec
3
.north
)
$}
\ExtractX
{$
(
softmax
3
.north
)
$}
\ExtractY
{$
(
decwordout.base
)
$}
\node
[wordnode,anchor=base]
() at (
\XCoord
,
\YCoord
)
{
know
}
;
\ExtractX
{$
(
dec
4
.north
)
$}
\ExtractX
{$
(
softmax
4
.north
)
$}
\ExtractY
{$
(
decwordout.base
)
$}
\node
[wordnode,anchor=base]
() at (
\XCoord
,
\YCoord
)
{
the
}
;
\ExtractX
{$
(
dec
5
.north
)
$}
\ExtractX
{$
(
softmax
5
.north
)
$}
\ExtractY
{$
(
decwordout.base
)
$}
\node
[wordnode,anchor=base]
() at (
\XCoord
,
\YCoord
)
{
way
}
;
\ExtractX
{$
(
dec
6
.north
)
$}
\ExtractX
{$
(
softmax
6
.north
)
$}
\ExtractY
{$
(
decwordout.base
)
$}
\node
[wordnode,anchor=base]
() at (
\XCoord
,
\YCoord
)
{
to
}
;
\ExtractX
{$
(
dec
7
.north
)
$}
\ExtractX
{$
(
softmax
7
.north
)
$}
\ExtractY
{$
(
decwordout.base
)
$}
\node
[wordnode,anchor=base]
() at (
\XCoord
,
\YCoord
)
{
Beijing
}
;
\ExtractX
{$
(
dec
8
.north
)
$}
\ExtractX
{$
(
softmax
8
.north
)
$}
\ExtractY
{$
(
decwordout.base
)
$}
\node
[wordnode,anchor=base]
() at (
\XCoord
,
\YCoord
)
{
Railway
}
;
\ExtractX
{$
(
dec
9
.north
)
$}
\ExtractX
{$
(
softmax
9
.north
)
$}
\ExtractY
{$
(
decwordout.base
)
$}
\node
[wordnode,anchor=base]
() at (
\XCoord
,
\YCoord
)
{
Station
}
;
\ExtractX
{$
(
dec
10
.north
)
$}
\ExtractX
{$
(
softmax
10
.north
)
$}
\ExtractY
{$
(
decwordout.base
)
$}
\node
[wordnode,anchor=base]
() at (
\XCoord
,
\YCoord
)
{$
\langle
$
eos
$
\rangle
$}
;
...
...
@@ -774,9 +785,15 @@ NLP问题的隐含结构假设 & 无隐含结构假设,端到端学习 \\
\draw
[-latex']
(enc
\x
.east) to (enc
\y
.west);
\draw
[-latex']
(dec
\x
.east) to (dec
\y
.west);
}
\coordinate
(bridge1) at ([yshift=-
\base
]demb5);
\coordinate
(bridge2) at ([yshift=-
\base
]demb2.west);
\draw
[-latex']
(enc10.north) .. controls +(north:0.8
\base
) and +(east:
\base
) .. (bridge1) to (bridge2) .. controls +(west:2.5
\base
) and +(west:0.6
\base
) .. (dec1.west);
\coordinate
(bridge) at ([yshift=-1.2
\base
]demb2);
\draw
[-latex']
(enc10.north) .. controls +(north:
\base
) and +(east:1.5
\base
) .. (bridge) .. controls +(west:2.5
\base
) and +(west:0.6
\base
) .. (dec1.west);
\end{scope}
% legend
\begin{scope}
[shift=
{
(-2.3
\base
,0)
}
]
\node
[rnnnode,minimum height=0.1\base,fill=green!30!white,label={[label distance=3pt,font=\scriptsize]
0:词嵌入层
}
] (emb) at (0,0)
{}
;
\node
[rnnnode,fill=blue!30!white,anchor=north west,label={[label distance=3pt,font=\scriptsize]
0:循环单元
}
] (rnn) at ([yshift=2.7
\base
]emb.south west)
{}
;
\node
[rnnnode,minimum height=0.1\base,fill=red!30!white,anchor=north west,label={[label distance=3pt,font=\scriptsize]
0:输出层
}
] (softmax) at ([yshift=2.6
\base
]rnn.south west)
{}
;
\end{scope}
\end{tikzpicture}
\end{center}
...
...
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