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update chapter 6

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正在显示 包含 141 行增加 和 1 行删除
Chapter6/Figures/figure-alignment-matrix-for-zh-to-en-translation.tex 0 → 100644
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Chapter6/Figures/figure-examples-of-sequential-translation-and-reorder-translation.tex 0 → 100644
%%% outline
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Chapter6/chapter6.tex
......@@ -41,7 +41,30 @@
%----------------------------------------------------------------------------------------
\subsection{什么是扭曲度}
可以考虑先用例子描述一下翻译中的调序，之后说单词调序对应着``调序距离''，这种距离实际上可以被看做是一种扭曲度。。。
\parinterval {\small\sffamily\bfseries{调序}}（Reordering）是自然语言翻译中特有的语言现象。造成这个现象的主要原因在于不同语言之间语序的差异，比如，汉语是“主谓宾”结构，而日语是“主宾谓”结构。即使在句子整体结构相似的语言上进行翻译，调序也是频繁出现的现象。如图\ref{fig:6-1}所示，当一个主动语态的汉语句子翻译为一个被动语态的英语句子时，如果直接顺序翻译，那么翻译结果“I with you am satisfied”很明显不符合英语语法。这里就需要采取一些方法和手段在翻译过程中对词或短语进行调序，从而得到正确的翻译结果。
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter6/Figures/figure-examples-of-sequential-translation-and-reorder-translation}
\caption{顺序翻译和调序翻译的实例对比}
\label{fig:6-1}
\end{figure}
%----------------------------------------------
\parinterval 既然调序时翻译中的基本现象，机器翻译自然就需要一种方式对其进行描述。其中，最常见的是基于“调序距离”的方法。这里，可以假设完全进行顺序翻译时，调序的“代价”是最低的。当调序出现时，可以用调序相对于顺序翻译产生的位置偏移来度量调序的程度，也被称为“调序距离”。图\ref{fig:6-2}展示了翻译时两种语言中词的对齐矩阵。比如，在图\ref{fig:6-2}(a)中，系统需要跳过“对”和“你”来翻译“感到”和“满意”，之后再回过头翻译“对”和“你”，这样就完成了对单词的调序。这时可以简单的把调序时需要跳过的单词数看作一种距离。
\parinterval 可以看到，调序距离实际上是在度量译文词序相对于源文词序的一种扭曲程度。因此，也常常把这种调序距离称作{\small\sffamily\bfseries{扭曲度}}（Distortion）。调序距离越大对应的扭曲度也越大。比如，可以明显看出图\ref{fig:6-2}（b）中调序的扭曲度要比图\ref{fig:6-2}（a）中调序的扭曲度大，因此\ref{fig:6-2}（b）实例的调序代价也更大。
\parinterval 在机器翻译中使用扭曲度进行翻译建模是一种十分自然的想法。接下来，会介绍两个基于扭曲度的翻译模型，分别是IBM模型2和隐马尔可夫模型。不同于IBM模型1，它们利用了单词的位置信息定义了扭曲度，并将扭曲度融入翻译模型中，使得对翻译问题的建模更加合理。
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter6/Figures/figure-alignment-matrix-for-zh-to-en-translation}
\caption{不同的译文导致不同IBM模型1得分的情况}
\label{fig:6-2}
\end{figure}
%----------------------------------------------
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