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Toy-MT-Introduction
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正在显示 包含 26 行增加 和 26 行删除
Book/Chapter1/chapter1.tex
......@@ -45,7 +45,7 @@
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter1/Figures/figure-Required-parts-of-MT}
\input{./Chapter1/Figures/figure-required-parts-of-mt}
\caption{机器翻译系统的组成}
\label{fig:1-2}
\end{figure}
......@@ -220,7 +220,7 @@
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter1/Figures/figure-Example-RBMT}
\input{./Chapter1/Figures/figure-example-rbmt}
\setlength{\belowcaptionskip}{-1.5em}
\caption{基于规则的机器翻译的示例图（左：规则库；右：规则匹配结果）}
\label{fig:1-8}
......@@ -290,7 +290,7 @@
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter1/Figures/figure-Example-SMT}
\input{./Chapter1/Figures/figure-example-smt}
\caption{统计机器翻译的示例图（左：语料资源；中：翻译模型与语言模型；右：翻译假设与翻译引擎）}
\label{fig:1-11}
\end{figure}
......@@ -311,7 +311,7 @@
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter1/Figures/figure-Example-NMT}
\input{./Chapter1/Figures/figure-example-nmt}
\caption{神经机器翻译的示例图（左：编码器-解码器网络；右：编码器示例网络）}
\label{fig:1-12}
\end{figure}
......
Book/Chapter2/chapter2.tex
......@@ -35,8 +35,8 @@
%----------------------------------------------
\begin{figure}[htp]
\centering
\subfigure[机器翻译系统被看作一个黑盒] {\input{./Chapter2/Figures/figure-MT-system-as-a-black-box} }
\subfigure[机器翻系统 = 前/后处理 + 翻译引擎] {\input{./Chapter2/Figures/figure-MT=language-analysis+translation-engine}}
\subfigure[机器翻译系统被看作一个黑盒] {\input{./Chapter2/Figures/figure-mt-system-as-a-black-box} }
\subfigure[机器翻系统 = 前/后处理 + 翻译引擎] {\input{./Chapter2/Figures/figure-mt=language-analysis+translation-engine}}
\caption{机器翻译系统的结构}
\label{fig:2-1}
\end{figure}
......@@ -125,7 +125,7 @@ F(X)=\int_{-\infty}^x f(x)dx
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter2/Figures/figure-Probability-density-function&Distribution-function}
\input{./Chapter2/Figures/figure-probability-density-function&distribution-function}
\caption{一个概率密度函数(左)与其对应的分布函数(右)}
\label{fig:2-3}
\end{figure}
......@@ -310,7 +310,7 @@ F(X)=\int_{-\infty}^x f(x)dx
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter2/Figures/figure-Self-information-function}
\input{./Chapter2/Figures/figure-self-information-function}
\caption{自信息函数$\textrm{I}(x)$关于$\textrm{P}(x)$的曲线}
\label{fig:2-6}
\end{figure}
......@@ -429,7 +429,7 @@ F(X)=\int_{-\infty}^x f(x)dx
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter2/Figures/figure-Example-of-word-segmentation-based-on-dictionary}
\input{./Chapter2/Figures/figure-example-of-word-segmentation-based-on-dictionary}
\caption{基于词典进行分词的实例}
\label{fig:2-8}
\end{figure}
......@@ -638,7 +638,7 @@ F(X)=\int_{-\infty}^x f(x)dx
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter2/Figures/figure-examples-of-Chinese-word-segmentation-based-on-1-gram-model}
\input{./Chapter2/Figures/figure-examples-of-chinese-word-segmentation-based-on-1-gram-model}
\caption{基于1-gram语言模型的中文分词实例}
\label{fig:2-17}
\end{figure}
......
Book/Chapter3/Chapter3.tex
......@@ -170,7 +170,7 @@
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter3/Figures/figure-processes-SMT}
\input{./Chapter3/Figures/figure-processes-smt}
\caption{简单的统计机器翻译流程}
\label{fig:3-5}
\end{figure}
......@@ -472,7 +472,7 @@ g(\mathbf{s},\mathbf{t}) \equiv \prod_{j,i \in \widehat{A}}{\textrm{P}(s_j,t_i)}
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter3/Figures/figure-greedy-MT-decoding-pseudo-code}
\input{./Chapter3/Figures/figure-greedy-mt-decoding-pseudo-code}
\caption{贪婪的机器翻译解码算法的伪代码}
\label{fig:3-10}
\end{figure}
......@@ -483,8 +483,8 @@ g(\mathbf{s},\mathbf{t}) \equiv \prod_{j,i \in \widehat{A}}{\textrm{P}(s_j,t_i)}
%----------------------------------------------
\begin{figure}[htp]
\centering
\subfigure{\input{./Chapter3/Figures/greedy-MT-decoding-process-1}}
\subfigure{\input{./Chapter3/Figures/greedy-MT-decoding-process-3}}
\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}
......
Book/Chapter4/chapter4.tex
......@@ -2162,7 +2162,7 @@ d_1 = {d'} \circ {r_5}
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter4/Figures/structure-of-Chart}
\input{./Chapter4/Figures/structure-of-chart}
\caption{Chart结构}
\label{fig:4-65}
\end{figure}
......@@ -2252,7 +2252,7 @@ d_1 = {d'} \circ {r_5}
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter4/Figures/content-of-Chart-in-tree-based-decoding}
\input{./Chapter4/Figures/content-of-chart-in-tree-based-decoding}
\caption{基于树的解码中Chart的内容}
\label{fig:4-68}
\end{figure}
......
Book/Chapter5/chapter5.tex
This source diff could not be displayed because it is too large. You can view the blob instead.
Book/Chapter7/Chapter7.tex
......@@ -90,7 +90,7 @@
%----------------------------------------------
\begin{figure}[htp]
\centering
\input{./Chapter7/Figures/figure-construction-steps-of-MT-system}
\input{./Chapter7/Figures/figure-construction-steps-of-mt-system}
\caption{构建神经机器翻译系统的主要步骤}
\label{fig:7-2}
\end{figure}
......@@ -417,7 +417,7 @@ y = f(x)
% 图7.
\begin{figure}[htp]
\centering
\input{./Chapter7/Figures/figure-Underfitting-vs-Overfitting}
\input{./Chapter7/Figures/figure-underfitting-vs-overfitting}
\caption{欠拟合 vs 过拟合}
\label{fig:7-11}
\end{figure}
......@@ -1191,7 +1191,7 @@ b &=& \omega_{\textrm{high}}\cdot |\mathbf{x}|
% 图7.5.1
\begin{figure}[htp]
\centering
\input{./Chapter7/Figures/Post-Norm-vs-Pre-Norm}
\input{./Chapter7/Figures/figure-post-norm-vs-pre-norm}
\caption{Post-Norm Transformer vs Pre-Norm Transformer}
\label{fig:7-28}
\end{figure}
......@@ -1273,7 +1273,7 @@ $g_l$会作为输入的一部分送入第$l+1$层。其网络的结构图\ref{fi
% 图7.5.2
\begin{figure}[htp]
\centering
\input{./Chapter7/Figures/dynamic-linear-aggregation-network-structure}
\input{./Chapter7/Figures/figure-dynamic-linear-aggregation-network-structure}
\caption{动态线性层聚合网络结构图}
\label{fig:7-29}
\end{figure}
......@@ -1299,7 +1299,7 @@ $g_l$会作为输入的一部分送入第$l+1$层。其网络的结构图\ref{fi
% 图7.5.3
\begin{figure}[htp]
\centering
\input{./Chapter7/Figures/progressive-training}
\input{./Chapter7/Figures/figure-progressive-training}
\caption{渐进式深层网络训练过程}
\label{fig:7-30}
\end{figure}
......@@ -1316,7 +1316,7 @@ $g_l$会作为输入的一部分送入第$l+1$层。其网络的结构图\ref{fi
% 图7.5.4
\begin{figure}[htp]
\centering
\input{./Chapter7/Figures/sparse-connections-between-different-groups}
\input{./Chapter7/Figures/figure-sparse-connections-between-different-groups}
\caption{不同组之间的稀疏连接}
\label{fig:7-31}
\end{figure}
......@@ -1335,7 +1335,7 @@ $g_l$会作为输入的一部分送入第$l+1$层。其网络的结构图\ref{fi
% 图7.5.5
\begin{figure}[htp]
\centering
\input{./Chapter7/Figures/learning-rate}
\input{./Chapter7/Figures/figure-learning-rate}
\caption{学习率重置vs从头训练的学习率曲线}
\label{fig:7-32}
\end{figure}
......@@ -1411,7 +1411,7 @@ p_l=\frac{l}{2L}\cdot \varphi
% 图7.5.7
\begin{figure}[htp]
\centering
\input{./Chapter7/Figures/expanded-residual-network}
\input{./Chapter7/Figures/figure-expanded-residual-network}
\caption{Layer Dropout中残差网络的展开图}
\label{fig:7-34}
\end{figure}
......
Book/mt-book-xelatex.tex
......@@ -122,13 +122,13 @@
% CHAPTERS
%----------------------------------------------------------------------------------------
\include{Chapter1/chapter1}
%\include{Chapter1/chapter1}
%\include{Chapter2/chapter2}
%\include{Chapter3/chapter3}
%\include{Chapter4/chapter4}
%\include{Chapter5/chapter5}
%\include{Chapter6/chapter6}
%\include{Chapter7/chapter7}
\include{Chapter7/chapter7}
%\include{ChapterAppend/chapterappend}
......
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