Update tables & RNN-MT figures

bf0af520 · Lee · 5518d0ce · bf0af520
Commit bf0af520 authored Nov 08, 2019 by Lee
--- a/Section06-Neural-Machine-Translation/section06.tex
+++ b/Section06-Neural-Machine-Translation/section06.tex
@@ -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
+\item 神经机器翻译在大部分场景下已经超越统计机器翻译！
-\only<1>{
+{
+    \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 & \\
+                & BLEU & HTER & mTER & \\
-                \midrule
+                \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
+                \specialrule{1pt}{1pt}{1pt}
-            \end{tabular}\\[4pt]
+            \end{tabular}\\
+            \addlinespace[-0.3ex]
            \tiny *Neural versus Phrase-Based Machine Translation Quality: a Case Study\\
        \end{tabular}
    \end{center}
 }
-\item Human party MT
+\item 微软报道在部分场景下机器翻译质量已经超越人类！
-\only<2>{
+{
+    \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
+                \specialrule{1pt}{1pt}{1pt}
-            \end{tabular}\\[4pt]
+            \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=1pt,minimum size=0.5\base,draw,inner sep=0pt,outer sep=0pt]
-            \tikzstyle{rnnnode} = [rounded corners=2pt,minimum width=0.5\base,minimum height=0.5\base,draw]
+        \tikzstyle{wordnode} = [font=\tiny]
-            \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) {$\langle$eos$\rangle$};
+            \node[wordnode,below=0pt of demb1] (decwordin) {$\langle$sos$\rangle$};
            \ExtractX{$(demb2.south)$}
            \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 dec1] (decwordout) {Do};
+            \node[wordnode,above=0pt of softmax1] (decwordout) {Do};
-            \ExtractX{$(dec2.north)$}
+            \ExtractX{$(softmax2.north)$}
            \ExtractY{$(decwordout.base)$}
            \node[wordnode,anchor=base] () at (\XCoord,\YCoord) {you};
-            \ExtractX{$(dec3.north)$}
+            \ExtractX{$(softmax3.north)$}
            \ExtractY{$(decwordout.base)$}
            \node[wordnode,anchor=base] () at (\XCoord,\YCoord) {know};
-            \ExtractX{$(dec4.north)$}
+            \ExtractX{$(softmax4.north)$}
            \ExtractY{$(decwordout.base)$}
            \node[wordnode,anchor=base] () at (\XCoord,\YCoord) {the};
-            \ExtractX{$(dec5.north)$}
+            \ExtractX{$(softmax5.north)$}
            \ExtractY{$(decwordout.base)$}
            \node[wordnode,anchor=base] () at (\XCoord,\YCoord) {way};
-            \ExtractX{$(dec6.north)$}
+            \ExtractX{$(softmax6.north)$}
            \ExtractY{$(decwordout.base)$}
            \node[wordnode,anchor=base] () at (\XCoord,\YCoord) {to};
-            \ExtractX{$(dec7.north)$}
+            \ExtractX{$(softmax7.north)$}
            \ExtractY{$(decwordout.base)$}
            \node[wordnode,anchor=base] () at (\XCoord,\YCoord) {Beijing};
-            \ExtractX{$(dec8.north)$}
+            \ExtractX{$(softmax8.north)$}
            \ExtractY{$(decwordout.base)$}
            \node[wordnode,anchor=base] () at (\XCoord,\YCoord) {Railway};
-            \ExtractX{$(dec9.north)$}
+            \ExtractX{$(softmax9.north)$}
            \ExtractY{$(decwordout.base)$}
            \node[wordnode,anchor=base] () at (\XCoord,\YCoord) {Station};
-            \ExtractX{$(dec10.north)$}
+            \ExtractX{$(softmax10.north)$}
            \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 (bridge) at ([yshift=-1.2\base]demb2);
-            \coordinate (bridge2) at ([yshift=-\base]demb2.west);
+            \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);
-            \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);
+        \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}