Commit 0bf1d5dc by xiaotong

Merge branch 'master' of 47.105.50.196:NiuTrans/mtbookv2

parents e3b3b0cf 62b6cd61
......@@ -19,6 +19,8 @@
\draw [<->,very thick] (s3.south) -- (m1.east);
\draw [<->,very thick] (s4.north) -- (m1.east);
\node [anchor=north] (l) at ([xshift=5em,yshift=-1em]s2.south) {{(a)}};
\end{scope}
......@@ -40,6 +42,8 @@
\draw [<->,very thick] (s1.south east) -- (s4.north west);
\draw [<->,very thick] (s2.north east) -- (s3.south west);
\node [anchor=north] (l) at ([xshift=5em,yshift=-1em]s2.south) {{(b)}};
\end{scope}
\end{tikzpicture}
......
......@@ -340,7 +340,7 @@
\begin{figure}[htp]
\centering
\input{./Chapter1/Figures/comparison-between-interlingua-based-and-transfer-based-translation}
\caption{基于中间语言的方法与基于转换的方法}
\caption{基于中间语言的方法(a)与基于转换的方法(b)}
\label{fig:1-13}
\end{figure}
%-------------------------------------------
......
%%% outline
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\node[anchor=east,inner sep=0pt,font=\footnotesize] at([xshift=-0.4em,yshift=-0.25cm-\bc*5]o.west){上班};
\node[anchor=west,inner sep=0pt,font=\footnotesize,rotate=45] at([xshift=0.1cm,yshift=0.4em]o.east){He};
\node[anchor=west,inner sep=0pt,font=\footnotesize,rotate=45] at([xshift=0.1cm+\bc,yshift=0.4em]o.east){takes};
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%---------------------------------------------------------------------
\ No newline at end of file
%%% outline
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%---------------------------------------------------------------------
\ No newline at end of file
......@@ -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}
%----------------------------------------------
%----------------------------------------------------------------------------------------
% NEW SUB-SECTION
......
......@@ -1364,6 +1364,63 @@
number={3},
year={1957},
}
@book{lowerre1976the,
title="The HARPY speech recognition system",
author="Bruce T. {Lowerre}",
notes="Sourced from Microsoft Academic - https://academic.microsoft.com/paper/2137095888",
year="1976"
}
@book{bishop1995neural,
title="Neural networks for pattern recognition",
author="Christopher M. {Bishop}",
notes="Sourced from Microsoft Academic - https://academic.microsoft.com/paper/1554663460",
year="1995"
}
@article{åström1965optimal,
title="Optimal control of Markov processes with incomplete state information",
author="Karl Johan {Åström}",
journal="Journal of Mathematical Analysis and Applications",
volume="10",
number="1",
pages="174--205",
notes="Sourced from Microsoft Academic - https://academic.microsoft.com/paper/2011418219",
year="1965"
}
@article{korf1990real,
title="Real-time heuristic search",
author="Richard E. {Korf}",
journal="Artificial Intelligence",
volume="42",
number="2",
pages="189--211",
notes="Sourced from Microsoft Academic - https://academic.microsoft.com/paper/2021061679",
year="1990"
}
@article{ney1994on,
title="On structuring probabilistic dependences in stochastic language modelling",
author="Hermann {Ney} and Ute {Essen} and Reinhard {Kneser}",
journal="Computer Speech \& Language",
volume="8",
number="1",
pages="1--38",
notes="Sourced from Microsoft Academic - https://academic.microsoft.com/paper/2075201173",
year="1994"
}
@inproceedings{ney1991on,
title="On smoothing techniques for bigram-based natural language modelling",
author="H. {Ney} and U. {Essen}",
booktitle="[Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing",
pages="825--828",
notes="Sourced from Microsoft Academic - https://academic.microsoft.com/paper/2020749563",
year="1991"
}
%%%%% chapter 2------------------------------------------------------
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
......
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