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Update herarchical phrase-based translation

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正在显示 包含 313 行增加 和 1 行删除
Section04-Phrasal-and-Syntactic-Models/section04.tex
...@@ -3586,12 +3586,77 @@ d = r_1 \circ r_2 \circ r_3 \circ r_4 ...@@ -3586,12 +3586,77 @@ d = r_1 \circ r_2 \circ r_3 \circ r_4
%%% 关于文法的约束 %%% 关于文法的约束
\begin{frame}{文法的约束} \begin{frame}{文法的约束}
% 把文法限制在合理的范围内，使得系统可以处理 % 把文法限制在合理的范围内，使得系统可以处理
\begin{itemize}
\item 按照上面的方法可以抽取到大量的规则，规则太多会降低训练和解码的效率，甚至影响翻译性能，因此需要加入一些\alert{约束}来限制文法规则的数目
\vspace{0.3em}
\begin{center}
\begin{tikzpicture}
\begin{scope}[minimum height = 18pt]
%\node[anchor=east] (s0) at (0, 0) {$\textrm{X} \ \to \ <\textrm{X}_1\ \textrm{希望}\ \textrm{X}_2,\ \textrm{X}_1\ \textrm{wish to}\ \textrm{X}_2>$};
\node[anchor=north west] (s1) at (0, 0) {$\textrm{X} \ \to \ <\textrm{X}_1\ \textrm{X}_2 \ \textrm{之一},\ \textrm{one of}\ \textrm{X}_1\ \textrm{X}_2>$};
\node[anchor=north west] (s2) at ([yshift=0.1em]s1.south west) {$\textrm{X} \ \to \ <\textrm{X}_1\ \textrm{X}_2\ \textrm{是}\ \textrm{X}_3,\ \textrm{X}_1\ \textrm{X}_2\ \textrm{is}\ \textrm{X}_3>$};
\node[anchor=north west] (s3) at ([yshift=0.1em]s2.south west) {$\textrm{X} \ \to \ <\textrm{X}_1\ \textrm{希望...}\ \textrm{X}_2,\ \textrm{X}_1\ \textrm{wish to...}\ \textrm{X}_2>$};
\draw[decorate,decoration={brace,amplitude=0.4em,mirror},red,thick] ([xshift=1.3em,yshift=0.3em]s3.south) -- ([xshift=8.0em,yshift=0.3em]s3.south);
\node[anchor=north] (wi) at ([xshift=4.7em,yshift=-0.0em]s3.south) {\scriptsize{\alert{超过10个词}}};
\visible<2->{
\draw[-,red] (s1.west)--(s1.east);
\draw[-,red] (s2.west)--(s2.east);
\draw[-,red] (s3.west)--(s3.east);
}
\end{scope}
\end{tikzpicture}
\end{center}
\vspace{-0.5em}
\visible<2->{
具体包含如下约束
}
\begin{enumerate}
\item<2-> 规则中的非终结符不可以连续的出现
\item<2-> 每条规则最多包含两个非终结符
\item<2-> 抽取规则最多可以跨越10个单词
\end{enumerate}
\item<3-> 除此之外，不同的语言会有不同的文法约束，可参考
\visible<3->{
\textbf{Hierarchical Phrase-Based Translation}\\
\textbf{Chiang, Computational Linguistics, 2007}
}
\end{itemize}
\end{frame} \end{frame}
%%%------------------------------------------------------------------------------------------------------------ %%%------------------------------------------------------------------------------------------------------------
%%% 特征 %%% 特征
\begin{frame}{特征} \begin{frame}{特征}
% 还是David Chiang的论文 % 还是David Chiang的论文
\begin{itemize}
\item 与短语模型一样，层次短语模型也使用判别式模型进行建模 - $\textrm{P}(d,\textbf{t}|\textbf{s}) = \frac{\exp(\sum_{i=1}^{M} \lambda_i \cdot h_i(d,\textbf{s},\textbf{t}))}{\sum_{d',t'}\exp(\sum_{i=1}^{M} \lambda_i \cdot h_i(d',\textbf{s},\textbf{t}'))}$。其中特征权重$\{\lambda_i\}$可以使用最小错误率训练进行调优，特征函数$\{h_i\}$需要用户定义。
\item<2-> 这里，所有层次短语规则满足$\langle\ \alpha, \beta, \sim\ \rangle$的形式
\begin{itemize}
\item $\alpha$和$\beta$表示源语和目标语的规则串，$\sim$表示他们的对应关系
\item 此外，定义$\tau(\alpha)$和$\tau(\beta)$为源语端和目标语端的规则序列。例如
\vspace{-0.8em}
\begin{eqnarray}
\tau(\alpha) & = & \textrm{对}\ \textrm{X}_1\ \textrm{感到}\ \textrm{X}_2 \nonumber \\
\tau(\beta) & = & \textrm{be}\ \textrm{X}_2\ \textrm{with}\ \textrm{X}_1 \nonumber
\end{eqnarray}
\end{itemize}
\item<3-> \textbf{特征1-2： 短语翻译概率}，即正向翻译概率$\Pr(\tau(\alpha)|\tau(\beta))$和反向翻译概率$\Pr(\tau(\alpha)|\tau(\beta))$。这里，$\tau(\alpha)$和$\tau(\beta)$ 都被看做短语，因此可以直接复用短语系统的方法，使用极大似然估计进行计算。
\end{itemize}
\end{frame}
%%%------------------------------------------------------------------------------------------------------------
%%% 特征
\begin{frame}{特征(续)}
% 给出特征列表
\begin{itemize}
\item \textbf{特征3-4： 词汇翻译概率}，即正向词汇翻译概率$\Pr_{lex}(\bar{t}|\bar{s})$和反向词汇翻译概率$\Pr_{lex}(\bar{s}|\bar{t})$。用来描述短语对中源语端单词和目标语端单词的对应关系
\item \textbf{特征5： $n$-gram语言模型}，即$\textrm{P}_{\textrm{lm}}(\textbf{t})$。度量译文的流畅度，可以使用大规模目标语单语数据得到。
\item<2-> \textbf{特征6：译文长度}，即$|\textbf{t}|$。避免模型倾向于短译文，同时让系统自动学习对译文长度的偏好。
\item<2-> \textbf{特征7：翻译规则数量}。这个特征是为了避免模型仅仅使用少量特征构成翻译推导(因为翻译概率相乘，因子少结果一般会大一些)，同时让系统自动学习对使用规则数量的偏好。
\item<2-> \textbf{特征8：源语言被翻译为空的单词数量}。注意，空翻译规则(或特征)有时也被称作evil feature，这类特征在一些数据集上对BLEU有很好的提升作用，但是会造成人工评价的下降，因此需要谨慎使用。
\end{itemize}
\end{frame} \end{frame}
%%%------------------------------------------------------------------------------------------------------------ %%%------------------------------------------------------------------------------------------------------------
...@@ -3601,6 +3666,129 @@ d = r_1 \circ r_2 \circ r_3 \circ r_4 ...@@ -3601,6 +3666,129 @@ d = r_1 \circ r_2 \circ r_3 \circ r_4
%%% CYK解码 %%% CYK解码
\begin{frame}{CYK解码} \begin{frame}{CYK解码}
% 看NiuTrans Manual % 看NiuTrans Manual
\begin{itemize}
\item 基于层次短语的翻译解码与基于短语的模型类似，都是要找到使$\textrm{score}(d)$达到最大的翻译推导$d$
\vspace{-0.5em}
\begin{displaymath}
\hat{d} = \argmax_{d \in D} \textrm{score}(d)
\end{displaymath}
\vspace{-0.8em}
\begin{itemize}
\item 由于翻译推导由SCFG构成，使用CYK算法进行解码
\item CYK算法解码是一个用来判定任意给定的字符串 是否属于一个上下文无关文法的算法，具体流程如下
\end{itemize}
\vspace{0.5em}
\begin{center}
\begin{tikzpicture}
\node [anchor=south west,rectangle,draw=ublue,thick,inner sep=0.4em,fill=white,drop shadow] (sourceG) at (0,0) {{\color{ublue} \footnotesize{\textbf{S端文法}}}};
\node [anchor=west,rectangle,draw=ublue,thick,inner sep=0.4em,fill=white,drop shadow] (chom) at ([xshift=3.5em]sourceG.east) {{\color{ublue} \footnotesize{\textbf{乔姆斯基范式}}}};
\node [anchor=west,rectangle,draw=ublue,thick,inner sep=0.4em,fill=white,drop shadow] (targetG) at ([xshift=3.5em]chom.east) {{\color{ublue} \footnotesize{\textbf{T端文法}}}};
\draw[->,very thick] ([xshift=0.1em]sourceG.east) -- ([xshift=-0.1em]chom.west);
\draw[->,very thick] ([xshift=-0.1em]targetG.west) -- ([xshift=0.1em]chom.east);
\node [anchor=north west,rectangle,draw=ublue,thick,inner sep=0.4em,fill=white,drop shadow,minimum height=1.2cm] (sourceS) at ([yshift=-1em]sourceG.south west) {{\color{ublue} \footnotesize{\textbf{S端句子}}}};
\node [anchor=north west,rectangle,draw=ublue,thick,inner sep=0.4em,fill=white,drop shadow,minimum height=1.2cm] (targetS) at ([yshift=-1em]targetG.south west) {{\color{ublue} \footnotesize{\textbf{T端句子}}}};
\node [anchor=north,rectangle,draw=ublue,thick,inner sep=0.4em,fill=white,drop shadow,minimum height=1.2cm] (parse) at ([yshift=-1em]chom.south) {{\color{ublue} \footnotesize{\textbf{解析得到最好的翻译}}}};
\draw[->,very thick] ([xshift=0.1em]sourceS.east) -- ([xshift=-0.1em]parse.west);
\draw[->,very thick] ([xshift=0.1em]parse.east) -- ([xshift=-0.1em]targetS.west);
\draw[->,very thick] ([yshift=-0.1em]chom.south) -- ([yshift=0.1em]parse.north);
\end{tikzpicture}
\end{center}
\vspace{0.3em}
\item 由于对文法中的非终结符进行了限制，可以直接使用CYK算法进行解码，无需转换成乔姆斯基范式
\end{itemize}
\end{frame}
%%%------------------------------------------------------------------------------------------------------------
%%% CYK解码
\begin{frame}{CYK解码（续）}
% 看NiuTrans Manual
\begin{itemize}
\item CYK解码提出了一种cell的数据结构，用来记录所有可能出现的翻译假设。
\begin{itemize}
\item<2-> 对于每个源语句子，使用短语规则表初始化它的cell
\item<3-> 自底向上对cell中的每个子cell进行重新组合（正向、反向）
\item<4-> 计算每个推导的得分并记录下来，最终选择最优推导所对应的译文作为输出
\end{itemize}
\end{itemize}
\vspace{-0.8em}
\begin{center}
\begin{tikzpicture}
\visible<2->{
\node [anchor=west] (s1) at (0,0) {\small{\textbf{进口}}};
\node [anchor=west] (s2) at ([xshift=4em]s1.east) {\small{\textbf{大幅度}}};
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\node [anchor=west] (t61) at ([xshift=3em,yshift=-1.8em]t53.west) {\scriptsize{have \ drastically \ fallen}};
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\node [anchor=west] (t71) at ([xshift=-6em,yshift=-1.8em]t62.west) {\scriptsize{The \ imports}};
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\end{tikzpicture}
\end{center}
\end{frame} \end{frame}
%%%------------------------------------------------------------------------------------------------------------ %%%------------------------------------------------------------------------------------------------------------
...@@ -3611,14 +3799,138 @@ d = r_1 \circ r_2 \circ r_3 \circ r_4 ...@@ -3611,14 +3799,138 @@ d = r_1 \circ r_2 \circ r_3 \circ r_4
\begin{frame}{立方剪枝(Cube Pruning)} \begin{frame}{立方剪枝(Cube Pruning)}
% 问题 % 问题
% 解决方法 % 解决方法
\begin{itemize}
\item 前面介绍的解码方法由于搜索空间非常大，速度很慢，通常使用剪枝的方法来加速这个过程
\begin{itemize}
\item 解码时对来自不同的cell进行合并时，第一个cell包含$n$个条目，第二个cell包含$m$个条目，就会产生$n \times m$个新条目
\vspace{0.8em}
\begin{center}
\begin{tikzpicture}
\node [anchor=west] (s1) at (0,0) {\footnotesize{$\textrm{X} \to <\textrm{从}\ \textrm{X}_1,\ \textrm{from}\ \textrm{X}_1>$}};
\node [anchor=east] (s2) at ([yshift=-2em]s1.east) {\footnotesize{$\textrm{X} \to <\textrm{从}\ \textrm{X}_1,\ \textrm{since}\ \textrm{X}_1>$}};
\node [anchor=east] (s3) at ([yshift=-2em]s2.east) {\footnotesize{$\textrm{X} \to <\textrm{从}\ \textrm{X}_1,\ \textrm{from the}\ \textrm{X}_1>$}};
\node [anchor=east] (s4) at ([yshift=-2em]s3.east) {\footnotesize{$\textrm{X} \to <\textrm{从}\ \textrm{X}_1,\ \textrm{through}\ \textrm{X}_1>$}};
\node [anchor=west] (t1) at ([xshift=2.5em]s1.east) {\footnotesize{$\textrm{X} \to <\textrm{计划},\ \textrm{plan}>$}};
\node [anchor=west] (t2) at ([xshift=2.5em]s2.east) {\footnotesize{$\textrm{X} \to <\textrm{方案},\ \textrm{scheme}>$}};
\node [anchor=west] (t3) at ([xshift=2.5em]s3.east) {\footnotesize{$\textrm{X} \to <\textrm{项目},\ \textrm{project}>$}};
\node [anchor=west] (t4) at ([xshift=2.5em]s4.east) {\footnotesize{$\textrm{X} \to <\textrm{时期},\ \textrm{times}>$}};
\foreach \x in {1,2,...,4}
\foreach \y in {1,2,...,4}
\draw[->] ([xshift=0.1em]s\x.east) -- ([xshift=-0.1em]t\y.west);
\begin{pgfonlayer}{background}
\node [rectangle,inner sep=0.3em,fill=red!20] [fit = (s1) (s3) (s4)] (box1) {};
\node [rectangle,inner sep=0.3em,fill=green!20] [fit = (t1) (t2) (t3) (t4)] (box2) {};
\end{pgfonlayer}
\end{tikzpicture}
\end{center}
\vspace{0.3em}
\item 当$n$和$m$都很大时，会产生很多新的条目，可以通过限制栈的大小来舍弃大部分条目
\item 另一个思路就是如何只生成有机会被选中的条目，这就是\alert{立方剪枝（cube pruning）}的思想
\end{itemize}
\end{itemize}
\end{frame}
%%%------------------------------------------------------------------------------------------------------------
%%% 立方剪枝
\begin{frame}{立方剪枝(Cube Pruning)(续)}
% 问题
% 解决方法
\begin{itemize}
\item 不是考虑所有可能的组合，而是从\alert{最优}候选开始选择
\begin{itemize}
\item 根据代价估计对每个cell中的条目进行排序，最好的候选在最顶层，次优候选排在后面
\item<2-> 计算所有邻居的代价，选择最优的条目，再计算其邻居的代价，不断进行迭代
\item<3-> 直到产生的条目达到一定的数量（如栈容量）或者新加入的条目被其他剪枝策略丢弃，则终止算法
\end{itemize}
\end{itemize}
\begin{center}
\begin{tikzpicture}
\tikzstyle{alignmentnode} = [rectangle,fill=blue!30,minimum size=0.4em,text=white,inner sep=0.1pt]
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\end{scope}
\end{tikzpicture}
\end{center}
\end{frame} \end{frame}
%%%------------------------------------------------------------------------------------------------------------ %%%------------------------------------------------------------------------------------------------------------
%%% BLEU等评价 %%% BLEU等评价
\begin{frame}{效果} \begin{frame}{效果}
% 实验结果 % 实验结果
\end{frame} \begin{itemize}
\item 从实验结果中可以看出，基于层次短语的翻译模型性能要优于基于短语的翻译模型
\item 选择使用层次短语信息实际上增加了模型的复杂度，但是可以通过借鉴基于短语的翻译模型模型以及CYK解码和立方剪枝等技术来解决
\item 可以考虑加入更多句法信息来进一步提升模型性能
\end{itemize}
%\vspace{-1em}
\begin{center}
\begin{tabular}{l | l | l | l}
\multicolumn{2}{c|}{模型} & 开发集 & 测试集 \\
\multicolumn{2}{c|}{} & (BLEU[\%]) & (BLEU[\%]) \\ \hline
\multicolumn{2}{l|}{短语(Moses)} & 36.51 & 34.93 \\
\multicolumn{2}{l|}{短语(NiuTrans)} & 36.99 & 35.29 \\ \hline
\multicolumn{2}{l|}{层次短语(Moses)} & 36.65 & 34.79 \\
\multicolumn{2}{l|}{层次短语(NiuTrans)} & 37.41 & 35.35 \\ \hline
\end{tabular}
\end{center}
\vspace{-0.5em}
\scriptsize{* 以上结果来自 NiuTrans: An Open Source Toolkit for Phrase-based Machine Translation}\\
\scriptsize{* 开发集：NIST MT03，测试集：NIST MT05}\\
\end{frame}
%%%------------------------------------------------------------------------------------------------------------ %%%------------------------------------------------------------------------------------------------------------
\section{基于语言学句法的模型} \section{基于语言学句法的模型}
......
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