Commit 65c39211 by xiaotong

re-submit

parent 1764d5c6
...@@ -28,6 +28,7 @@ ...@@ -28,6 +28,7 @@
\usetikzlibrary{calc,intersections} \usetikzlibrary{calc,intersections}
\usetikzlibrary{matrix} \usetikzlibrary{matrix}
\usetikzlibrary{patterns}
\usetikzlibrary{arrows,decorations.pathreplacing} \usetikzlibrary{arrows,decorations.pathreplacing}
\usetikzlibrary{shadows} % LATEX and plain TEX when using Tik Z \usetikzlibrary{shadows} % LATEX and plain TEX when using Tik Z
\usetikzlibrary{shadows.blur} \usetikzlibrary{shadows.blur}
...@@ -144,30 +145,170 @@ ...@@ -144,30 +145,170 @@
\subsection{注意力机制} \subsection{注意力机制}
%%%------------------------------------------------------------------------------------------------------------ %%%------------------------------------------------------------------------------------------------------------
%%% 如何定义注意力函数 %%% 解码 - beam search
\begin{frame}{计算注意力权重 - 注意力函数} \begin{frame}{推断 - Beam Search}
\begin{itemize} \begin{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:\\ \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
\vspace{-0.3em} \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
\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$$h$之间的\alert{相似性},这也可以被看作是目标语表示和源语言表示的一种``统一化'',即把源语言和目标语表示在同一个语义空间,进而语义相近的内容有更大的相似性。\visible<3->{定义$a(s,h)$的方式:}
\visible<3->{
\begin{displaymath}
a(s,h) = \left\{ \begin{array}{ll}
s h^T & \textrm{向量乘} \\
\textrm{cos}(s, h) & \textrm{向量夹角} \\
s \textbf{W} h^T & \textrm{线性模型} \\
\textrm{TanH}(\textbf{W}[s,h])\textbf{v}^T & \textrm{拼接}
\end{array}
\right.
\end{displaymath}
$\textbf{W}$$\textbf{v}$是可学习参数
}
\end{itemize} \end{itemize}
\vspace{-0.3em}
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\begin{center}
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\begin{scope}
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\node [rnnnode,anchor=south,fill=blue!20] (o2) at ([yshift=1em]s2.north) {\tiny{softmax ($\times 3$)}};
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\node [rnnnode,anchor=south,fill=blue!20] (o3) at ([yshift=1em]s3.north) {\tiny{softmax ($\times 3$)}};
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\draw [->] ([yshift=0.1em]o\x.north) -- ([yshift=0.8em]o\x.north) node [pos=0.5,right] {\tiny{top-3}};
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\foreach \x in {3}{
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\node [anchor=north] (cap) at (vocab.south east) {\scriptsize{\textbf{束搜索($b=3$)}}};
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}
\end{scope}
\end{tikzpicture}
\end{center}
}
\end{frame} \end{frame}
%%%------------------------------------------------------------------------------------------------------------ %%%------------------------------------------------------------------------------------------------------------
......
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