Commit efe053c9 by 曹润柘

update: figures modification and problem solving

parent be087cd2
......@@ -197,7 +197,7 @@
%\end{figure}
%---------------------------
\parinterval 看一个具体的例子,如例\ref{example3-1}所示,有一个汉英互译的句对$(\mathbf{s},\mathbf{t})$。假设,$x=\textrm{``翻译''}$$y=\textrm{``translation''}$,现在要计算$x$$y$共现的总次数。``翻译''和``translation''分别在$\mathbf{s}$$\mathbf{t}$中出现了2次,因此$c(\textrm{``翻译''},\textrm{``translation''};\mathbf{s},\mathbf{t})$ 等于4。而对于$\sum_{x',y'} c(x',y';\mathbf{s},\mathbf{t})$,因为$x'$$y'$分别表示的是$s$$t$中的任意词,所以$\sum_{x',y'} c(x',y';\mathbf{s},\mathbf{t})$表示所有单词对的数量 - 即$\mathbf{s}$的词数乘以$\mathbf{t}$的词数。最后,“翻译”和“translation”的单词翻译概率为:
\parinterval 看一个具体的例子,如例\ref{example3-1}所示,有一个汉英互译的句对$(\mathbf{s},\mathbf{t})$。假设,$x=\textrm{``翻译''}$$y=\textrm{``translation''}$,现在要计算$x$$y$共现的总次数。``翻译''和``translation''分别在$\mathbf{s}$$\mathbf{t}$中出现了2次,因此$c(\textrm{``翻译''},\textrm{``translation''};\mathbf{s},\mathbf{t})$ 等于4。而对于$\sum_{x',y'} c(x',y';\mathbf{s},$ $\mathbf{t})$,因为$x'$$y'$分别表示的是$s$$t$中的任意词,所以$\sum_{x',y'} c(x',y';\mathbf{s},\mathbf{t})$表示所有单词对的数量 - 即$\mathbf{s}$的词数乘以$\mathbf{t}$的词数。最后,“翻译”和“translation”的单词翻译概率为:
\begin{eqnarray}
\textrm{P}(\text{``翻译''},\text{``translation''}; \mathbf{s},\mathbf{t}) & = & \frac{c(\textrm{``翻译''},\textrm{``translation''};\mathbf{s},\mathbf{t})}{\sum_{x',y'} c(x',y';\mathbf{s},\mathbf{t})} \nonumber \\
& = & \frac{4}{|s|\times |t|} \nonumber \\
......@@ -365,7 +365,7 @@ g(s,t) \equiv \prod_{j,i \in \widehat{A}}{\textrm{P}(s_j,t_i)} \times \textrm{P
\label{eqC3.12-new}
\end{eqnarray}
\noindent 其中$\argmax_{t} \textrm{P}(t|s)$表示找到使$\textrm{P}(t|s)$达到最大时的译文$t$。结合上一小节中关于$\textrm{P}(t|s)$的定义,把公式\ref{eqC3.7-new}带入公式\ref{eqC3.12-new}得到:
\noindent 其中$\argmax_{t} \textrm{P}(t|s)$表示找到使$\textrm{P}(t|s)$达到最大时的译文$t$。结合上一小节中关于$\textrm{P}(t|s)$\\的定义,把公式\ref{eqC3.7-new}带入公式\ref{eqC3.12-new}得到:
\begin{eqnarray}
\widehat{t}=\argmax_{t}\frac{g(s,t)}{\sum_{t^{'}g(s,t^{'})}}
\label{eqC3.13-new}
......@@ -411,12 +411,14 @@ g(s,t) \equiv \prod_{j,i \in \widehat{A}}{\textrm{P}(s_j,t_i)} \times \textrm{P
\label{fig:3-10}
\end{figure}
%---------------------------
\vspace{5.0em}
%----------------------------------------------
% 图3.13
\begin{figure}[htp]
\centering
\subfigure{\input{./Chapter3/Figures/greedy-MT-decoding-process-1}}
\subfigure{\input{./Chapter3/Figures/greedy-MT-decoding-process-3}}
\setlength{\belowcaptionskip}{14.0em}
\caption{贪婪的机器翻译解码过程实例}
\label{fig:3-11}
\end{figure}
......@@ -671,7 +673,7 @@ g(s,t) \equiv \prod_{j,i \in \widehat{A}}{\textrm{P}(s_j,t_i)} \times \textrm{P
\end{figure}
%---------------------------
\parinterval 这样就得到了IBM模型1中句子翻译概率的计算式。可以看出IBM模型1的假设把翻译模型化简成了非常简单的形式。对于给定的$s$$a$$t$,只要知道$\varepsilon$$t(s_j |t_(a_j ))$ 就可以计算出$\textrm{P}(s|t)$,进而求出$\textrm{P}(s|t)$\\ \\ \\
\parinterval 这样就得到了IBM模型1中句子翻译概率的计算式。可以看出IBM模型1的假设把翻译模型化简成了非常简单的形式。对于给定的$s$$a$$t$,只要知道$\varepsilon$$t(s_j |t_{a_j })$ 就可以计算出$\textrm{P}(s|t)$,进而求出$\textrm{P}(s|t)$\\ \\ \\
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{IBM模型2}\index{Chapter3.4.2}
......@@ -719,7 +721,7 @@ g(s,t) \equiv \prod_{j,i \in \widehat{A}}{\textrm{P}(s_j,t_i)} \times \textrm{P
\label{eqC3.29-new}
\end{eqnarray}
\noindent 公式\ref{eqC3.29-new}的技巧在于把若干个乘积的加法(等式左手端)转化为若干加法结果的乘积(等式右手端),这样省去了多次循环,把$O((l+1)^m m)$的计算复杂度降为$(l+1)m$\footnote{公式\ref{eqC3.29-new}相比公式\ref{eqC3.28-new}的另一个优点在于,公式\ref{eqC3.29-new}中乘法的数量更少,因为现代计算机中乘法运算的强度要高于加法,因此公式\ref{eqC3.29-new}的计算机实现效率更高。}\ref{fig:3-21}对这个过程进行了进一步解释。
\noindent 公式\ref{eqC3.29-new}的技巧在于把若干个乘积的加法(等式左手端)转化为若干加法结果的乘积(等式右手端),这样省去了多次循环,把$O((l+1)^m m)$的计算复杂度降为$(l+1)m$\footnote{公式\ref{eqC3.29-new}相比公式\ref{eqC3.28-new}的另一个优点在于,公式\ref{eqC3.29-new}中乘法的数量更少,因为现代计算机中乘法运算的强度要高于加法,因此公式\ref{eqC3.29-new}的计算机实现效率更高。}\ref{fig:3-21}对这个过程进行了进一步解释。
%----------------------------------------------
% 图3.32-new
......@@ -860,7 +862,7 @@ f(s_u|t_v) = \lambda_{t_v}^{-1} \frac{\epsilon}{(l+1)^{m}} \prod\limits_{j=1}^{m
\end{figure}
%---------------------------
\noindent\hspace{2em} 为了化简$f(s_u|t_v)$的计算,在此对公式\ref{eqC3.40-new}进行了重新组织,见下图。红色部分表示翻译概率P$(s|t)$;蓝色部分表示$(s_u,t_v)$在句对$(s,t)$中配对的总次数,即$t_v$翻译为$s_u$‘在所有对齐中出现的次数;绿色部分表示$f(s_u|t_v)$对于所有的$t_i$的相对值,即’$t_v$翻译为$s_u$‘在所有对齐中出现的相对概率;蓝色与绿色部分相乘表示“$t_v$翻译为$s_u$这个事件出现次数的期望的估计,称之为期望频次(expected count)。
\noindent\hspace{2em} 为了化简$f(s_u|t_v)$的计算,在此对公式\ref{eqC3.40-new}进行了重新组织,见下图。红色部分表示翻译概率P$(s|t)$;蓝色部分表示$(s_u,t_v)$在句对$(s,t)$中配对的总次数,即`$t_v$翻译为$s_u$'在所有对齐中出现的次数;绿色部分表示$f(s_u|t_v)$对于所有的$t_i$的相对值,即`$t_v$翻译为$s_u$'在所有对齐中出现的相对概率;蓝色与绿色部分相乘表示``$t_v$翻译为$s_u$''这个事件出现次数的期望的估计,称之为期望频次(expected count)。
%----------------------------------------------
% 图3.29
\begin{figure}[htp]
......@@ -1304,6 +1306,7 @@ p_0+p_1 & = & 1 \label{eqC3.62-new}
\end{itemize}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{appendices}
\chapter{附录A}
\label{appendix-A}
\section{IBM模型3训练方法}
......@@ -1486,11 +1489,8 @@ d_{>1}(\Delta_i|cb,v;\bm{s},\bm{t}) = \mu_{>1cb}^{-1} \times \sum_{s=1}^{S}c_{>1
\end{eqnarray}
%----------------------------------------------
\parinterval 这里$\tilde{\tilde{b}}(\bm{a})$借用了模型4中$\tilde{b}(\bm{a})$的概念。不过$\tilde{\tilde{b}}(\bm{a})$表示在利用模型3进行排名的列表中满足$\textrm{p}_{\theta}(\bm{a}'|\bm{s},\bm{t};5)$的最高排名的词对齐。
\parinterval
\end{appendices}
......
......@@ -6,14 +6,11 @@
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......@@ -34,41 +31,43 @@
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......@@ -90,28 +89,33 @@
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......
......@@ -4,7 +4,7 @@
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......@@ -17,7 +17,7 @@
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......@@ -40,13 +40,7 @@
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......@@ -68,34 +62,41 @@
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}
\draw [->,thick] ([xshift=-0.1em]t1.north) -- (phi1.south);
\draw [->,thick] ([xshift=0.05em]t2.north) -- (phi2.south);
\draw [->,thick] ([xshift=0.05em]t3.north) -- (phi3.south);
\draw [->,thick] ([xshift=0.05em]t4.north) -- (phi4.south);
\draw [->,thick] ([xshift=0.05em]t5.north) -- (phi5.south);
\node [rectangle,draw,anchor=north,line width=1pt,minimum height=2.5em,minimum width=5.5em] (t1) at ([yshift=-15em]sf1.south) {$t_0$};
\node [rectangle,draw,anchor=north,line width=1pt,minimum height=2.5em,minimum width=5.5em] (t2) at ([yshift=-15em]s1.south) {Scientists};
\node [rectangle,draw,anchor=north,line width=1pt,minimum height=2.5em,minimum width=5.5em] (t3) at ([yshift=-15em]s2.south) {do};
\node [rectangle,draw,anchor=north,line width=1pt,minimum height=2.5em,minimum width=5.5em] (t4) at ([yshift=-15em]s3.south) {not};
\node [rectangle,draw,anchor=north,line width=1pt,minimum height=2.5em,minimum width=5.5em] (t5) at ([yshift=-15em]s4.south) {konw};
}
\draw [->,thick] (tau23.east) -- (s2.south);
\draw [->,thick] (t1.north) -- ([yshift=-8.8em]sf1.south);
\draw [->,thick] (t2.north) -- ([yshift=-8.8em]s1.south);
\draw [->,thick] (t3.north) -- ([yshift=-8.8em]s2.south);
\draw [->,thick] (t4.north) -- ([yshift=-8.8em]s3.south);
\draw [->,thick] (t5.north) -- ([yshift=-8.8em]s4.south);
\draw [->,thick] ([yshift=4.6em]t1.north) -- ([yshift=-4.4em]sf1.south);
\draw [->,thick] ([yshift=4.6em]t2.north) -- ([yshift=-4.4em]s1.south);
\draw [->,thick] ([yshift=4.6em]t3.north) -- ([yshift=-4.4em]s2.south);
\draw [->,thick] ([yshift=4.6em]t4.north) -- ([yshift=-4.4em]s3.south);
\draw [->,thick] ([yshift=4.6em]t5.north) -- ([yshift=-4.4em]s4.south);
\draw [->,thick] ([yshift=9em]t2.north) -- (s1.south);
\draw [->,thick] ([yshift=9em]t4.north) -- (s3.south);
\draw [->,thick] ([yshift=9em]t5.north) -- (s4.south);
{\scriptsize
\node [anchor=west] (sent11) at ([xshift=1em,yshift=-1em]s4.south east) {把这些元语};
\node [anchor=west] (sent11) at ([xshift=1em,yshift=-2em]s4.south east) {把这些元语};
\node [anchor=west] (sent12) at ([yshift=-1em]sent11.west) {言单词放在};
\node [anchor=west] (sent13) at ([yshift=-1em]sent12.west) {合适的位置};
\node [anchor=west] (sent21) at ([yshift=-3em]sent13.west) {确定生成元};
......
......@@ -20,12 +20,12 @@
{
{\footnotesize
\node [rectangle,draw,anchor=west,line width=1pt,minimum height=2em,minimum width=4em] (t1) at ([yshift=-5.5em]s11.west) {$t_0$};
\node [rectangle,draw,anchor=west,line width=1pt,minimum height=2em,minimum width=4em] (t2) at ([xshift=2em]t1.east) {I};
\node [rectangle,draw,anchor=west,line width=1pt,minimum height=2em,minimum width=4em] (t3) at ([xshift=2em]t2.east) {changed};
\node [rectangle,draw,anchor=west,line width=1pt,minimum height=2em,minimum width=4em] (t4) at ([xshift=2em]t3.east) {my};
\node [rectangle,draw,anchor=west,line width=1pt,minimum height=2em,minimum width=4em] (t5) at ([xshift=2em]t4.east) {mind};
\node [rectangle,draw,anchor=west,line width=1pt,minimum height=2em,minimum width=4em] (t6) at ([xshift=2em]t5.east) {.};
\node [anchor=north,rectangle,draw,line width=1pt,minimum height=2em,minimum width=4em] (t1) at ([yshift=-3.5em]s11.south) {$t_0$};
\node [rectangle,draw,anchor=north,line width=1pt,minimum height=2em,minimum width=4em] (t2) at ([yshift=-3.5em]s1.south) {I};
\node [rectangle,draw,anchor=north,line width=1pt,minimum height=2em,minimum width=4em] (t3) at ([yshift=-3.5em]s2.south) {changed};
\node [rectangle,draw,anchor=north,line width=1pt,minimum height=2em,minimum width=4em] (t4) at ([yshift=-3.5em]s3.south) {my};
\node [rectangle,draw,anchor=north,line width=1pt,minimum height=2em,minimum width=4em] (t5) at ([yshift=-3.5em]s4.south) {mind};
\node [rectangle,draw,anchor=north,line width=1pt,minimum height=2em,minimum width=4em] (t6) at ([yshift=-3.5em]s5.south) {.};
\node [anchor=north] (nd1) at (t2.south) {[1]};
\node [anchor=north] (nd2) at (t3.south) {[2]};
......
......@@ -55,6 +55,7 @@
\IfFileExists{C:/WINDOWS/win.ini}
{\newcommand{\mycfont}{song}}
{\newcommand{\mycfont}{gbsn}}
\begin{CJK}{UTF8}{\mycfont}
\end{CJK}
......
......@@ -550,7 +550,7 @@ addtohook={%
\usepackage{booktabs}
\usepackage{bm}
\usetikzlibrary{shapes.misc}
\usepackage{appendix}
......
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