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Chapter16/Figures/figure-cycle-consistency.tex deleted 100644 → 0
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翻译模型 \\
$\textrm{P}(\ \mathbi{y}|\ \mathbi{x})$
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今天天气真好。
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The weather is \\so good today.
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翻译模型 \\
$\textrm{P}(\ \mathbi{x}|\ \mathbi{y})$
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\ No newline at end of file
Chapter16/Figures/figure-elmo-model-structure.tex deleted 100644 → 0
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{
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\node [anchor=south,embedding,fill=yellow!20](node3-2) at ([yshift=2em]node2-4.north){\footnotesize{$\seq{P}_2$}};
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Chapter16/Figures/lm-fusion.tex deleted 100644 → 0
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\node [minicir,anchor=west] (a11-1) at ([xshift=0.8em]a12.east) {\tiny{$\beta$}};
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\node [anchor=west] (a15-2) at ([xshift=-4.25em]a15.west) {\tiny{$\cdots$}};
\node [anchor=east] (a13-3) at ([yshift=0.8em]a13-2.west) {\small{模型语言}};
\node [anchor=north] (a13-4) at ([xshift=0em]a13-3.south) {\small{隐藏层}};
\node [anchor=east] (a14-3) at ([yshift=0.8em]a14-2.west) {\small{神经机器翻译}};
\node [anchor=north] (a14-4) at ([xshift=0.5em]a14-3.south) {\small{模型隐藏层}};
\node [anchor=east] (a15-3) at ([xshift=0em]a15-2.west) {\small{上下文向量}};
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%%%%%%%%%%%%%%%%%%%%%%
\node [cir,anchor=west] (a2) at ([xshift=10.0em]a1.east) {\tiny{$y_{t}$}};
\node [add,anchor=north] (a21) at ([yshift=-1em]a2.south) {\tiny{$+$}};
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\node[anchor=north](pos1) at ([xshift=-1.5em,yshift=-0.5em]a15.south) {(a) 浅融合};
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Chapter16/chapter16.tex
......@@ -372,7 +372,7 @@
\begin{itemize}
\vspace{0.5em}
\item 基于枢轴语言的方法，即以资源丰富的语言（通常为英语、汉语等）为中心，在语言对之间进行翻译\upcite{DBLP:conf/emnlp/KimPPKN19}（{\color{red} 参考文献：Pivot Language Approach for Phrase-Based Statistical Machine Translation}）；
\item 基于枢轴语言的方法，即以资源丰富的语言（通常为英语、汉语等）为中心，在语言对之间进行翻译\upcite{DBLP:conf/emnlp/KimPPKN19,DBLP:journals/mt/WuW07}；
\vspace{0.5em}
\item 基于知识蒸馏的方法，即用枢轴语言到目标语言的训练指导源语言到目标语言的训练\upcite{DBLP:conf/acl/ChenLCL17}；
\vspace{0.5em}
......@@ -388,7 +388,7 @@
\subsection{基于枢轴语言的方法}
\parinterval 传统的多语言翻译中，广泛使用的是{\small\bfnew{基于枢轴语言的翻译}}（Pivot-based Translation）\upcite{DBLP:conf/emnlp/KimPPKN19}（{\color{red} 参考文献：Pivot Language Approach for Phrase-Based Statistical Machine Translation}）。在这种方法中，会使用一种数据丰富语言作为{\small\bfnew{中介语言}}\index{中介语言}或者{\small\bfnew{枢轴语言}}\index{枢轴语言}（Pivot Language）\index{Pivot Language}，之后让源语言和目标语言向枢轴语言进行翻译。这样，通过资源丰富的中介语言将源语言和目标语言桥接在一起，达到解决源语言-目标语言双语数据缺乏的问题。比如，想要得到泰语到波兰语的翻译，可以通过英语做枢轴语言。通过“泰语$\to$英语$\to$波兰语”的翻译过程完成泰语到波兰语的转换。
\parinterval 传统的多语言翻译中，广泛使用的是{\small\bfnew{基于枢轴语言的翻译}}（Pivot-based Translation）\upcite{DBLP:conf/emnlp/KimPPKN19,DBLP:journals/mt/WuW07}。在这种方法中，会使用一种数据丰富语言作为{\small\bfnew{中介语言}}\index{中介语言}或者{\small\bfnew{枢轴语言}}\index{枢轴语言}（Pivot Language）\index{Pivot Language}，之后让源语言和目标语言向枢轴语言进行翻译。这样，通过资源丰富的中介语言将源语言和目标语言桥接在一起，达到解决源语言-目标语言双语数据缺乏的问题。比如，想要得到泰语到波兰语的翻译，可以通过英语做枢轴语言。通过“泰语$\to$英语$\to$波兰语”的翻译过程完成泰语到波兰语的转换。
\parinterval 基于枢轴语的方法很早就出现在基于统计机器翻译中。在基于短语的机器翻译中，已经有很多方法建立了源到枢轴和枢轴到目标的短语/单词级别特征，并基于这些特征开发了源语言到目标语言的系统\upcite{DBLP:conf/naacl/UtiyamaI07,DBLP:journals/mt/WuW07,DBLP:conf/acl/ZahabiBK13,DBLP:conf/emnlp/ZhuHWZWZ14,DBLP:conf/acl/MiuraNSTN15}，这些系统也已经广泛用于翻译稀缺资源语言对\upcite{DBLP:conf/acl/CohnL07,DBLP:journals/mt/WuW07,DBLP:conf/acl/WuW09,de2006catalan}。由于基于枢轴语的方法与模型结构无关，因此该方法也快速适用于神经机器翻译，并且取得了不错的效果\upcite{DBLP:conf/emnlp/KimPPKN19,DBLP:journals/corr/ChengLYSX16}。
......
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