new pages

Section06-Neural-Machine-Translation/section06-test.tex

@@ -28,7 +28,6 @@
 
 \usetikzlibrary{calc,intersections}
 \usetikzlibrary{matrix}
-\usetikzlibrary{patterns}
 \usetikzlibrary{arrows,decorations.pathreplacing}
 \usetikzlibrary{shadows} % LATEX and plain TEX when using Tik Z
 \usetikzlibrary{shadows.blur}
@@ -145,170 +144,30 @@
 \subsection{注意力机制}
 
 %%%------------------------------------------------------------------------------------------------------------
-%%% 解码 - beam search
-\begin{frame}{推断 - Beam Search}
+%%% 如何定义注意力函数
+\begin{frame}{计算注意力权重 - 注意力函数}
 \begin{itemize}
-\item \textbf{Greedy Search}: 目标语每一个位置，输出层的Softmax可以得到所有单词的概率，然后选择一个概率最大单词输出，下一个位置的预测就基于这一步输出的单词
-\item \textbf{Beach Search}: 为了避免贪婪方法造成的错误累加，可以每次对$b$个单词进行扩展，而不是只使用一个单词，其中$b$称做束的宽度 - 这样可以搜索更多可能的译文
-\end{itemize}
-
+\item 再来看一下注意力权重的定义。这个过程实际上是对$a(\cdot,\cdot)$做指数归一化：\\
 \vspace{-0.3em}
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-
-\visible<4->{
-\node [anchor=north] (cap) at (vocab.south east) {\scriptsize{\textbf{束搜索($b=3$)}}};
-}
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-
-
-\end{scope}
-\end{tikzpicture}
-\end{center}
-}
-
+\begin{displaymath}
+\alpha_{i,j} = \frac{\exp(a(s_{i-1}, h_j))}{\sum_{j'} \exp(a(s_{i-1}, h_{j'}))}
+\end{displaymath}
+
+\item<2-> 注意力函数$a(s,h)$的目的是捕捉$s$和$h$之间的\alert{相似性}，这也可以被看作是目标语表示和源语言表示的一种``统一化''，即把源语言和目标语表示在同一个语义空间，进而语义相近的内容有更大的相似性。\visible<3->{定义$a(s,h)$的方式：}
+    
+    \visible<3->{
+    \begin{displaymath}
+    a(s,h) =  \left\{ \begin{array}{ll}
+    s h^T & \textrm{向量乘} \\
+    \textrm{cos}(s, h) & \textrm{向量夹角} \\
+    s \textbf{W} h^T & \textrm{线性模型} \\
+    \textrm{TanH}(\textbf{W}[s,h])\textbf{v}^T & \textrm{拼接}
+    \end{array}
+    \right.
+    \end{displaymath}
+    $\textbf{W}$和$\textbf{v}$是可学习参数
+    }
+\end{itemize}
 \end{frame}
 
 %%%------------------------------------------------------------------------------------------------------------