new update

Section06-Neural-Machine-Translation/section06.tex

@@ -198,7 +198,7 @@
 %%% 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 神经机器翻译在大部分场景下已经超越统计机器翻译！
+\item 神经机器翻译在很多场景下已经超越统计机器翻译
 {
     \footnotesize
     \begin{center}
@@ -223,7 +223,7 @@
         \end{tabular}
     \end{center}
 }
-\item 微软报道在部分场景下机器翻译质量已经超越人类！
+\item 微软的报道：在部分场景下机器翻译质量已经接近甚至超过人工翻译
 {
     \footnotesize
     \begin{center}
@@ -239,7 +239,6 @@
                 \specialrule{0.6pt}{1pt}{1pt}
                 人工翻译 & 68.6 & REFERENCE-HT \\
                 & 67.6 & REFERENCE-PE \\
-                & 62.1 & REFERENCE-WMT \\
                 \specialrule{1pt}{1pt}{1pt}
             \end{tabular}\\
             \addlinespace[-0.3ex]
@@ -651,7 +650,7 @@ NLP问题的隐含结构假设 & 无隐含结构假设，端到端学习 \\
 \begin{itemize}
 \item 2015年前统计机器翻译(SMT)在NLP是具有统治力的
     \begin{itemize}
-    \item 当时NMT的系统还很初级，被SMT碾压
+    \item 当时的NMT系统还很初级，被SMT碾压
     \item 大多数的认知还没有进化到NMT时代，甚至Kalchbrenner等人早期的报告也被人质疑
     \end{itemize}
 \item 2016年情况大有改变，当时非常受关注的一项工作是Google上线了神经机器翻译系统GNMT
@@ -674,7 +673,8 @@ NLP问题的隐含结构假设 & 无隐含结构假设，端到端学习 \\
     \item 解码端也是一个RNN，利用编码结果逐词解码出译文
     \end{itemize}
 \end{itemize}
-%%% 图
+
+\vspace{-0.5em}
 \begin{center}
     \begin{tikzpicture}
         \newlength{\base}
@@ -688,7 +688,7 @@ NLP问题的隐含结构假设 & 无隐含结构假设，端到端学习 \\
             % RNN Encoder
             \coordinate (eemb0) at (0,0);
             \foreach \x [count=\y from 0] in {1,2,...,10}
-                \node[rnnnode,minimum height=0.1\base,fill=green!30!white,anchor=west] (eemb\x) at ([xshift=0.4\base]eemb\y.east) {};
+                \node[rnnnode,minimum height=0.5\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=0.4\base of enc1] (init) {$0$};
@@ -706,11 +706,11 @@ 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,minimum height=0.1\base,fill=green!30!white,anchor=south] (demb\x) at ([yshift=2\base]enc\x.north) {};
+                \node[rnnnode,minimum height=0.5\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) {};
+                \node[rnnnode,minimum height=0.5\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) {$\langle$sos$\rangle$};
@@ -790,10 +790,16 @@ NLP问题的隐含结构假设 & 无隐含结构假设，端到端学习 \\
         \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) {};
+        \begin{scope}[shift={(10\base,2.5\base)}]
+            \node[rnnnode,minimum height=0.5\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\base]emb.south west) {};
+            \node[rnnnode,minimum height=0.5\base,fill=red!30!white,anchor=north west,label={[label distance=3pt,font=\scriptsize]0:输出层}] (softmax) at ([yshift=2\base]rnn.south west) {};
+            \node [anchor=north west] (softmax2) at ([xshift=0.6\base]softmax.south west) {\scriptsize{Softmax}};
+            \node [anchor=north west] (rnn2) at ([xshift=0.6\base]rnn.south west) {\scriptsize{LSTM}};
+
+            \node [anchor=west] (reprlabel) at ([xshift=1em]enc10.east) {\scriptsize{句子表示}};
+            \draw [->,dashed] (reprlabel.west) -- ([xshift=0.1em]enc10.east);
+            \node [rnnnode,fill=purple!30!white] at (enc10) {};
         \end{scope}
     \end{tikzpicture}
 \end{center}