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Chapter10/chapter10.tex
......@@ -272,7 +272,7 @@ NMT & 21.7 & 18.7 & -13.7 \\
\parinterval 编码器-解码器框架是一种典型的基于“表示”的模型。编码器的作用是将输入的文字序列通过某种转换变为一种新的“表示”形式，这种“表示”包含了输入序列的所有信息。之后，解码器把这种“表示”重新转换为输出的文字序列。这其中的一个核心问题是表示学习，即：如何定义对输入文字序列的表示形式，并自动学习这种表示，同时应用它生成输出序列。一般来说，不同的表示学习方法可以对应不同的机器翻译模型，比如，在最初的神经机器翻译模型中，源语言句子都被表示为一个独立的向量，这时表示结果是静态的；而在注意力机制中，源语言句子的表示是动态的，也就是翻译目标语言的每个单词时都会使用不同的表示结果。
\parinterval 图\ref{fig:10-5}是一个应用编码器-解码器结构来解决机器翻译问题的简单实例。给定一个中文句子“我/对/你/感到/满意”，编码器会将这句话编码成一个实数向量$(0.2, -1, 6, 5, 0.7, -2)$，这个向量就是源语言句子的“表示”结果。虽然有些不可思议，但是神经机器翻译模型把这个向量等同于输入序列。向量中的数字并没有实际的意义，然而解码器却能从中提取到源语言句子中所包含的信息。也有研究者把向量的每一个维度看作是一个“特征”，这样源语言句子就被表示成多个“特征”的联合，而且这些特征可以被自动学习。有了这样的源语言句子的“表示”，解码器可以把这个实数向量作为输入，然后逐词生成目标语言句子“I am satisfied with you”。
\parinterval 图\ref{fig:10-5}是一个应用编码器-解码器结构来解决机器翻译问题的简单实例。给定一个中文句子“我/对/你/感到/满意”，编码器会将这句话编码成一个实数向量$(0.2, -1, 6, \\ 5, 0.7, -2)$，这个向量就是源语言句子的“表示”结果。虽然有些不可思议，但是神经机器翻译模型把这个向量等同于输入序列。向量中的数字并没有实际的意义，然而解码器却能从中提取到源语言句子中所包含的信息。也有研究者把向量的每一个维度看作是一个“特征”，这样源语言句子就被表示成多个“特征”的联合，而且这些特征可以被自动学习。有了这样的源语言句子的“表示”，解码器可以把这个实数向量作为输入，然后逐词生成目标语言句子“I am satisfied with you”。
%----------------------------------------------
\begin{figure}[htp]
......@@ -966,7 +966,7 @@ L(\mathbi{Y},\widehat{\mathbi{Y}}) = \sum_{j=1}^n L_{\textrm{ce}}(\mathbi{y}_j,\
\parinterval 神经网络的参数主要是各层中的线性变换矩阵和偏置。在训练开始时，需要对参数进行初始化。但是，由于神经机器翻译的网络结构复杂，因此损失函数往往不是凸函数，不同初始化会导致不同的优化结果。而且在大量实践中已经发现，神经机器翻译模型对初始化方式非常敏感，性能优异的系统往往需要特定的初始化方式。
\parinterval 因为LSTM是神经机器翻译中常用的一种模型，下面以LSTM模型为例（见\ref{sec:lstm-cell}节），介绍机器翻译模型的初始化方法，这些方法也可以推广到GRU等结构。具体内容如下：
\parinterval 因为LSTM是神经机器翻译中常用的一种模型，下面以LSTM模型为例（见\ref{sec:lstm-cell}\\ 节），介绍机器翻译模型的初始化方法，这些方法也可以推广到GRU等结构。具体内容如下：
\begin{itemize}
\vspace{0.5em}
......
Chapter11/Figures/figure-convolution-kernel.tex
......@@ -51,9 +51,9 @@
%\node[minimum width = 1.8cm] (sub) at ([xshift=-5.5cm,yshift=2cm]num9_9.east) {};
\draw[decorate,decoration={brace,mirror,raise=0pt,amplitude=0.3cm},black,thick] ([yshift=0.4cm,xshift=-0.1cm]num1_1.west) -- node[att,xshift=-0.5cm]{$q$} ([yshift=-0.4cm,xshift=-0.1cm]num3_3.west);
\draw[decorate,decoration={brace,raise=0pt,amplitude=0.3cm},black,thick] ([xshift=-0.4cm,yshift=0.1cm]num1.north) -- node[att,yshift=0.5cm]{$k$}([xshift=0.4cm,yshift=0.1cm]num7.north);
\draw[decorate,decoration={brace,mirror,raise=0pt,amplitude=0.3cm},black,thick] ([xshift=0.5cm,yshift=0.00cm]num9_9.south) -- node[att,xshift=0.5cm,yshift=-0.3cm]{$o$}([xshift=0.5cm,yshift=0.00cm]num9.south);
\draw[decorate,decoration={brace,mirror,raise=0pt,amplitude=0.3cm},black,thick] ([yshift=0.4cm,xshift=-0.1cm]num1_1.west) -- node[att,xshift=-0.5cm]{$Q$} ([yshift=-0.4cm,xshift=-0.1cm]num3_3.west);
\draw[decorate,decoration={brace,raise=0pt,amplitude=0.3cm},black,thick] ([xshift=-0.4cm,yshift=0.1cm]num1.north) -- node[att,yshift=0.5cm]{$U$}([xshift=0.4cm,yshift=0.1cm]num7.north);
\draw[decorate,decoration={brace,mirror,raise=0pt,amplitude=0.3cm},black,thick] ([xshift=0.5cm,yshift=0.00cm]num9_9.south) -- node[att,xshift=0.5cm,yshift=-0.3cm]{$O$}([xshift=0.5cm,yshift=0.00cm]num9.south);
\end{tikzpicture}
\ No newline at end of file
Chapter11/Figures/figure-deep-vs-light.tex
......@@ -21,10 +21,10 @@
\draw[line width=0.9pt, gray!80, -latex] (l\point_3.east) -- (r2_3.west);
}
\node[vuale] at (-1.5em, 1.9em) {$x_2$};
\node[vuale] at (-1.5em, 9.9em) {$x_1$};
\node[vuale] at (6.5em, 1.9em) {$y_1$};
\node[vuale] at (6.5em, 9.9em) {$y_2$};
\node[vuale] at (-1.5em, 1.9em) {$\mathbi{x}_2$};
\node[vuale] at (-1.5em, 9.9em) {$\mathbi{x}_1$};
\node[vuale] at (6.5em, 1.9em) {$\mathbi{z}_2$};
\node[vuale] at (6.5em, 9.9em) {$\mathbi{z}_1$};
\node (t2) at (2.5em, -1em) {\large{$\cdots$}};
\node [anchor=north,font=\tiny] at ([yshift=-0.2em]t2.south) {深度卷积};
......@@ -46,10 +46,10 @@
\draw[line width=0.9pt, cyan!80, -latex] (l\point_3.east) -- (r2_3.west);
}
\node[vuale] at (-1.5em, 1.9em) {$x_2$};
\node[vuale] at (-1.5em, 9.9em) {$x_1$};
\node[vuale] at (6.5em, 1.9em) {$y_1$};
\node[vuale] at (6.5em, 9.9em) {$y_2$};
\node[vuale] at (-1.5em, 1.9em) {$\mathbi{x}_2$};
\node[vuale] at (-1.5em, 9.9em) {$\mathbi{x}_1$};
\node[vuale] at (6.5em, 1.9em) {$\mathbi{z}_2$};
\node[vuale] at (6.5em, 9.9em) {$\mathbi{z}_1$};
\node (t2) at (2.5em, -1em) {\large{$\cdots$}};
\node [anchor=north,font=\tiny] at ([yshift=-0.2em]t2.south) {轻量卷积};
......
Chapter11/Figures/figure-single-glu.tex
......@@ -63,9 +63,9 @@ $\otimes$： & 按位乘运算 \\
\draw[-latex,thick] (b.east) -- (c2.west);
\draw[-latex,thick] (c2.east) -- ([xshift=0.4cm]c2.east);
\node[inner sep=0pt, font=\tiny] at (0.75cm, -0.4cm) {$X$};
\node[inner sep=0pt, font=\tiny] at ([yshift=-0.4cm]a.south) {$B=X * V + c$};
\node[inner sep=0pt, font=\tiny] at ([yshift=-0.4cm]b.south) {$A=X * W + b$};
\node[inner sep=0pt, font=\tiny] at (8.5cm, -0.4cm) {$Y=A \otimes \sigma(B)$};
\node[inner sep=0pt, font=\tiny] at (0.75cm, -0.4cm) {$\mathbi{X}$};
\node[inner sep=0pt, font=\tiny] at ([yshift=-0.4cm]a.south) {$\mathbi{B}=\mathbi{X} * \mathbi{V} + \mathbi{b}_{\mathbi{W}}$};
\node[inner sep=0pt, font=\tiny] at ([yshift=-0.4cm]b.south) {$\mathbi{A}=\mathbi{X} * \mathbi{W} + \mathbi{b}_{\mathbi{V}}$};
\node[inner sep=0pt, font=\tiny] at (8.5cm, -0.4cm) {$\mathbi{Y}=\mathbi{A} \otimes \sigma(\mathbi{B})$};
\end{tikzpicture}
\ No newline at end of file
Chapter11/Figures/figure-standard.tex
......@@ -32,12 +32,12 @@
\draw[line width=0.5pt, cyan!80, -latex] (l3_\point.east) -- ([xshift=0em,yshift=0.1em]r3_2.west);
}
\node[vuale] at ([xshift=-0.9em]l1_1.west) {$x_3$};
\node[vuale] at ([xshift=-0.9em]l2_1.west) {$x_2$};
\node[vuale] at ([xshift=-0.9em]l3_1.west) {$x_1$};
\node[vuale] at ([xshift=0.9em]r1_1.east) {$y_3$};
\node[vuale] at ([xshift=0.9em]r2_1.east) {$y_3$};
\node[vuale] at ([xshift=0.9em]r3_1.east) {$y_3$};
\node[vuale] at ([xshift=-0.9em]l1_1.west) {$\mathbi{x}_3$};
\node[vuale] at ([xshift=-0.9em]l2_1.west) {$\mathbi{x}_2$};
\node[vuale] at ([xshift=-0.9em]l3_1.west) {$\mathbi{x}_1$};
\node[vuale] at ([xshift=0.9em]r1_1.east) {$\mathbi{z}_3$};
\node[vuale] at ([xshift=0.9em]r2_1.east) {$\mathbi{z}_2$};
\node[vuale] at ([xshift=0.9em]r3_1.east) {$\mathbi{z}_1$};
\node (t1) at (2.5em, -1em) {\large{$\cdots$}};
\node [anchor=north,font=\tiny] at ([yshift=-0.2em]t1.south) {传统卷积};
......@@ -66,12 +66,12 @@
\draw[line width=0.5pt, cyan!80, -latex] (l\point_2.east) -- (r3_2.west);
}
\node[vuale] at ([xshift=-0.9em]l1_1.west) {$x_3$};
\node[vuale] at ([xshift=-0.9em]l2_1.west) {$x_2$};
\node[vuale] at ([xshift=-0.9em]l3_1.west) {$x_1$};
\node[vuale] at ([xshift=0.9em]r1_1.east) {$y_3$};
\node[vuale] at ([xshift=0.9em]r2_1.east) {$y_3$};
\node[vuale] at ([xshift=0.9em]r3_1.east) {$y_3$};
\node[vuale] at ([xshift=-0.9em]l1_1.west) {$\mathbi{x}_3$};
\node[vuale] at ([xshift=-0.9em]l2_1.west) {$\mathbi{x}_2$};
\node[vuale] at ([xshift=-0.9em]l3_1.west) {$\mathbi{x}_1$};
\node[vuale] at ([xshift=0.9em]r1_1.east) {$\mathbi{z}_3$};
\node[vuale] at ([xshift=0.9em]r2_1.east) {$\mathbi{z}_2$};
\node[vuale] at ([xshift=0.9em]r3_1.east) {$\mathbi{z}_1$};
\node (t2) at (2.5em, -1em) {\large{$\cdots$}};
\node [anchor=north,font=\tiny] at ([yshift=-0.2em]t2.south) {深度卷积};
......@@ -102,12 +102,12 @@
\draw[line width=0.5pt, cyan!80, -latex] (l3_\point.east) -- (r3_2.west);
}
\node[vuale] at ([xshift=-0.9em]l1_1.west) {$x_3$};
\node[vuale] at ([xshift=-0.9em]l2_1.west) {$x_2$};
\node[vuale] at ([xshift=-0.9em]l3_1.west) {$x_1$};
\node[vuale] at ([xshift=0.9em]r1_1.east) {$y_3$};
\node[vuale] at ([xshift=0.9em]r2_1.east) {$y_3$};
\node[vuale] at ([xshift=0.9em]r3_1.east) {$y_3$};
\node[vuale] at ([xshift=-0.9em]l1_1.west) {$\mathbi{x}_3$};
\node[vuale] at ([xshift=-0.9em]l2_1.west) {$\mathbi{x}_2$};
\node[vuale] at ([xshift=-0.9em]l3_1.west) {$\mathbi{x}_1$};
\node[vuale] at ([xshift=0.9em]r1_1.east) {$\mathbi{z}_3$};
\node[vuale] at ([xshift=0.9em]r2_1.east) {$\mathbi{z}_2$};
\node[vuale] at ([xshift=0.9em]r3_1.east) {$\mathbi{z}_1$};
\node (t3) at (2.5em, -1em) {\large{$\cdots$}};
\node [anchor=north,font=\tiny] at ([yshift=-0.2em]t3.south) {逐点卷积};
......
Chapter11/Figures/figure-structural-comparison-a.tex
......@@ -12,8 +12,8 @@
\node(num7)[num,right of = num6,xshift = 1.2cm]{\textcolor{blue!70}{$\mathbi{e}_7$}};
\node(num8)[num,right of = num7,xshift = 1.2cm]{\textcolor{blue!70}{$\mathbi{e}_8$}};
\node(num9)[num,right of = num8,xshift = 1.2cm]{$\mathbi{e}_9$};
\node(A)[below of = num2,yshift = -0.6cm]{A};
\node(B)[below of = num8,yshift = -0.6cm]{B};
%\node(A)[below of = num2,yshift = -0.6cm]{A};
%\node(B)[below of = num8,yshift = -0.6cm]{B};
\draw [->, thick, color = blue!80](num2.east)--(num3.west);
......@@ -23,5 +23,8 @@
\draw [->, thick, color = blue!80](num6.east)--(num7.west);
\draw [->, thick, color = blue!80](num7.east)--(num8.west);
\draw [->,thick,color = black!70] (num1) -- (num2);
\draw [->,thick,color =black!70] (num8) -- (num9);
\end{tikzpicture}
\ No newline at end of file
Chapter11/Figures/figure-structural-comparison-b.tex
......@@ -13,8 +13,8 @@
\node(num1_8)[num,right of = num1_7,xshift = 1.2cm]{\textcolor{blue!70}{$\mathbi{e}_8$}};
\node(num1_9)[num,right of = num1_8,xshift = 1.2cm]{$\mathbi{e}_9$};
\node(num1_10)[num,right of = num1_9,xshift = 1.2cm, fill = blue!40]{$\mathbi{0}$};
\node(A)[below of = num2,yshift = -0.6cm]{A};
\node(B)[below of = num8,yshift = -0.6cm]{B};
%\node(A)[below of = num2,yshift = -0.6cm]{A};
%\node(B)[below of = num8,yshift = -0.6cm]{B};
\node(num2_0)[num,above of = num1_0,yshift = 1.2cm, fill = blue!40]{\textcolor{white}{$\mathbi{0}$}};
\node(num2_1)[num,right of = num2_0,xshift = 1.2cm]{\textbf2};
......
Chapter11/chapter11.tex
......@@ -26,7 +26,7 @@
\parinterval 卷积神经网络是一种经典的神经计算模型，在计算机视觉等领域已经得到广泛应用。通过卷积、池化等一系列操作，卷积神经网络可以很好的对输入数据进行特征提取。这个过程也与图像和语言加工中局部输入信号的处理有着天然的联系。而卷积操作也可以被多次执行，形成多层卷积神经网络，进而进行更高层次的特征抽象。
\parinterval 在自然语言处理中，卷积神经网络也是备受关注的模型之一。本章将介绍基于卷积神经网络的机器翻译模型。重点介绍如何利用卷积神经网络构建端到端翻译模型。同时，对一些机器翻译中改进的卷积神经网络结构进行讨论。
\parinterval 在自然语言处理中，卷积神经网络也是备受关注的模型之一。本章将介绍基于卷积神经网络的机器翻译模型，不仅会重点介绍如何利用卷积神经网络构建端到端翻译模型，也会对一些机器翻译中改进的卷积神经网络结构进行讨论。
%----------------------------------------------------------------------------------------
% NEW SECTION
......@@ -83,7 +83,7 @@
\end{figure}
%----------------------------------------------
\parinterval 在图像卷积中，卷积核是一组$q \times k \times o$的参数（如图\ref{fig:11-3}）。其中$q$和$k$表示卷积核窗口的长度与宽度，分别对应图像中的长和宽两个维度，$q \times k$决定了该卷积核窗口的大小。$o$是该卷积核的深度，它的取值和输入数据通道数保持一致。在这里，通道可以看作图像不同的特征，比如灰色图像只有灰度信息，通道数为1；而RGB格式的图像有3个通道，分别对应红绿蓝三种颜色信息。
\parinterval 在图像卷积中，卷积核是一组$Q \times U \times O$的参数（如图\ref{fig:11-3}）。其中$Q$和$U$表示卷积核窗口的长度与宽度，分别对应图像中的长和宽两个维度，$Q \times U$决定了该卷积核窗口的大小。$O$是该卷积核的深度，它的取值和输入数据通道数保持一致。在这里，通道可以看作图像不同的特征，比如灰色图像只有灰度信息，通道数为1；而RGB格式的图像有3个通道，分别对应红绿蓝三种颜色信息。
%----------------------------------------------
% 图4.
......@@ -174,16 +174,16 @@
\begin{figure}[htp]
\centering
%\input{./Chapter11/Figures/figure-f }
\subfigure[循环神经网络的串行结构（$O(n)$）]{\input{./Chapter11/Figures/figure-structural-comparison-a}}
\subfigure[卷积神经网络的层级结构（$O(n/k)$）]{\input{./Chapter11/Figures/figure-structural-comparison-b}}
\subfigure[循环神经网络的串行结构]{\input{./Chapter11/Figures/figure-structural-comparison-a}}
\subfigure[卷积神经网络的层级结构]{\input{./Chapter11/Figures/figure-structural-comparison-b}}
\caption{串行及层级结构对比（$\mathbi{e}_i$表示词嵌入，$\mathbi{0}$表示$\mathbi{0}$向量，2,3,4表示第几层）}
\label{fig:11-9}
\end{figure}
%----------------------------------------------
\parinterval 针对不定长序列，一种可行的方法是使用之前介绍过的循环神经网络，其本质也是基于权重共享的想法，在不同的时间步复用相同的循环神经网络单元进行处理。但是，循环神经网络最大的弊端在于每一时刻的计算都依赖于上一时刻的结果，因此只能对序列进行串行处理，无法充分利用硬件设备进行并行计算，导致效率相对较低。此外，在处理较长的序列时，这种串行的方式很难捕捉长距离的依赖关系。与之对应的卷积神经网络，由于采用卷积这种局部处理并且共享参数的操作，可以对序列高效地并行处理。同时，针对序列中距离较长的依赖关系，可以通过堆叠多层卷积层来扩大{\small\bfnew{感受野}}\index{感受野} (Receptive Field)\index{Receptive Field} ，这里感受野指能够影响神经元输出的原始输入数据区域的大小。这种堆叠的层级结构对比串行的方式具有更短的路径及非线性计算（如图\ref{fig:11-9}），更容易进行训练。因此，也有许多研究人员在许多自然语言处理任务上尝试使用卷积神经网络进行序列建模\upcite{Kim2014ConvolutionalNN,Santos2014DeepCN,Kalchbrenner2014ACN,DBLP:conf/naacl/Johnson015,DBLP:conf/naacl/NguyenG15}。
\parinterval 针对不定长序列，一种可行的方法是使用之前介绍过的循环神经网络，其本质也是基于权重共享的想法，在不同的时间步复用相同的循环神经网络单元进行处理。但是，循环神经网络最大的弊端在于每一时刻的计算都依赖于上一时刻的结果，因此只能对序列进行串行处理，无法充分利用硬件设备进行并行计算，导致效率相对较低。此外，在处理较长的序列时，这种串行的方式很难捕捉长距离的依赖关系。与之对应的卷积神经网络，由于采用卷积这种局部处理并且共享参数的操作，可以对序列高效地并行处理。同时，针对序列中距离较长的依赖关系，可以通过堆叠多层卷积层来扩大{\small\bfnew{感受野}}\index{感受野} (Receptive Field)\index{Receptive Field} ，这里感受野指能够影响神经元输出的原始输入数据区域的大小。图\ref{fig:11-9}对比了这两种结构，可以看出，为了捕捉$\mathbi{e}_2$和$\mathbi{e}_8$之间的联系，串行结构需要顺序的6次操作，和长度相关。而层级结构仅需要4层卷积计算，和卷积核的大小相关，相比于串行的方式具有更短的路径和更少的非线性计算，更容易进行训练。因此，也有许多研究人员在许多自然语言处理任务上尝试使用卷积神经网络进行序列建模\upcite{Kim2014ConvolutionalNN,Santos2014DeepCN,Kalchbrenner2014ACN,DBLP:conf/naacl/Johnson015,DBLP:conf/naacl/NguyenG15}。
\parinterval 区别于传统图像上的卷积操作，在面向序列的卷积操作中，卷积核只在序列这一维度进行移动，用来捕捉多连续词之间的特征。需要注意的是，由于单词通常由一个实数向量表示（词嵌入），因此可以将词嵌入的维度看作是卷积操作中的通道数。图\ref{fig:11-10}就是一个基于序列卷积的文本分类模型，模型使用多个不同（包括尺度不同）的卷积核来对序列进行特征提取，得到了多个不同的特征序列。然后使用池化层降低表示维度，得到了一组和序列长度无关的特征表示。基于这组压缩过的特征表示，模型再通过全连接网络和Softmax函数作为相应类别的预测。在这其中卷积层和池化层分别起到了特征提取和状态压缩的作用，将一个不定长的序列转化到一组固定大小的特征表示。
\parinterval 区别于传统图像上的卷积操作，在面向序列的卷积操作中，卷积核只在序列这一维度进行移动，用来捕捉多连续词之间的特征。需要注意的是，由于单词通常由一个实数向量表示（词嵌入），因此可以将词嵌入的维度看作是卷积操作中的通道数。图\ref{fig:11-10}就是一个基于序列卷积的文本分类模型，模型使用多个不同（对应图中不同的颜色）的卷积核来对序列进行特征提取，得到了多个不同的特征序列。然后使用池化层降低表示维度，得到了一组和序列长度无关的特征表示。基于这组压缩过的特征表示，模型再通过全连接网络和Softmax函数作为相应类别的预测。在这其中卷积层和池化层分别起到了特征提取和状态压缩的作用，将一个不定长的序列转化到一组固定大小的特征表示。
%----------------------------------------------
% 图10.
......@@ -195,7 +195,7 @@
\end{figure}
%----------------------------------------------
\parinterval 和其它自然语言处理任务不同的是，机器翻译中需要对序列进行全局表示，换句话说，模型需要捕捉序列中各个位置之间的关系。因此，基于卷积网络的神经机器翻译模型需要堆叠多个卷积层进行远距离的依赖关系的建模。同时，为了在多层网络中维持序列的原有长度，需要在卷积操作前对输入序列进行填充。图\ref{fig:11-11}是一个简单的示例，针对一个长度$M=6$的句子，其隐层表示维度即卷积操作的输入通道数是$O=4$，卷积核大小为$K=3$。首先对序列进行填充，得到一个长度为8的序列，然后使用这些卷积核在这之上进行特征提取。一共使用了$N=4$个卷积核，整体的参数量为$K \times O \times N$，最后的卷积结果为$M \times N$的序列表示。
\parinterval 和其它自然语言处理任务不同的是，机器翻译中需要对序列进行全局表示，换句话说，模型需要捕捉序列中各个位置之间的关系。因此，基于卷积网络的神经机器翻译模型需要堆叠多个卷积层进行远距离的依赖关系的建模。同时，为了在多层网络中维持序列的原有长度，需要在卷积操作前对输入序列进行填充。图\ref{fig:11-11}是一个简单的示例，针对一个长度$m=6$的句子，其隐层表示维度即卷积操作的输入通道数是$O=4$，卷积核大小为$K=3$。首先对序列进行填充，得到一个长度为8的序列，然后使用这些卷积核在这之上进行特征提取。一共使用了$N=4$个卷积核，整体的参数量为$K \times O \times N$，最后的卷积结果为$m \times N$的序列表示。
%----------------------------------------------
% 图11.
......@@ -220,13 +220,13 @@
\parinterval 图\ref{fig:11-12}为ConvS2S模型的结构示意图，其内部由若干不同的模块组成，包括：
\begin{itemize}
\item {\small\bfnew{位置编码}}（Position Embedding）：图中绿色背景框表示源语端词嵌入部分，相比于RNN中的词嵌入（Word Embedding），该模型还引入了位置编码，帮助模型获得词位置信息。
\item {\small\bfnew{位置编码}}（Position Embedding）：图中绿色背景框表示源语端词嵌入部分，相比于RNN中的词嵌入（Word Embedding），该模型还引入了位置编码，帮助模型获得词位置信息。位置编码具体实现在图\ref{fig:11-12}中没有显示，详见\ref{sec:11.2.1}节。
\item {\small\bfnew{卷积层与门控线性单元}}（Gated Linear Units, GLU）：黄色背景框是卷积模块，这里使用门控线性单元作为非线性函数，之前的研究工作\upcite{Dauphin2017LanguageMW}表明这种非线性函数更适合于序列建模任务。图中为了简化，只展示了一层卷积，但在实际中为了更好地捕获句子信息，通常使用多层卷积叠加计算。
\item {\small\bfnew{残差连接}}\index{残差连接}（Residual Connection）\index{Residual Connection}：对于源语言端和目标语言端的卷积层网络之间，都存在一个从输入到输出的额外连接，即跨层连接\upcite{DBLP:journals/corr/HeZRS15}。该连接方式确保每个隐层输出都能包含输入序列中的更多信息，同时能够有效促进深层网络的信息传递效率（该部分在图\ref{fig:11-12}中没有显示，具体结构详见\ref{sec:11.2.3}节）。
\item {\small\bfnew{多跳注意力机制}}\index{多跳注意力机制}(Multi-step Attention)\index{Multi-step Attention}：蓝色框内部展示了基于多跳结构的注意力机制模块\upcite{Sukhbaatar2015EndToEndMN}。使用多跳注意力机制可以帮助解码器最后一层的卷积模块关注源语言和目标语言信息；同时每一层卷积层都会执行同样的注意力机制，这样可以帮助模型获得更多历史信息。下面我们将以此模型为例对基于CNN的机器翻译模型进行介绍。
\item {\small\bfnew{多跳注意力机制}}\index{多跳注意力机制}(Multi-step Attention)\index{Multi-step Attention}：蓝色框内部展示了基于多跳结构的注意力机制模块\upcite{Sukhbaatar2015EndToEndMN}。ConvS2S模型同样使用注意力机制来捕捉源语言，区别于之前的做法，多跳注意力在解码端每一个层都会执行注意力操作。下面我们将以此模型为例对基于CNN的机器翻译模型进行介绍。
\end{itemize}
%----------------------------------------------
......@@ -244,6 +244,7 @@
%----------------------------------------------------------------------------------------
\subsection{位置编码}
\label{sec:11.2.1}
\parinterval 和基于循环神经网络的翻译模型类似，基于卷积神经网络的翻译模型同样采用词嵌入来进行输入序列的表示，对于模型语言序列$\seq{w}=\{\mathbi{w}_1,\mathbi{w}_2,...,\mathbi{w}_m\}$，序列$\seq{w}$是维度大小为$m \times d$的矩阵，第$i$个单词$\mathbi{w}_i$是维度为$d$的向量，其中$m$为序列长度，$d$为词嵌入向量维度。和循环神经网络不同的是，基于卷积神经网络的模型需要对每个输入单词位置进行表示。这是由于，在卷积神经网络中，受限于卷积核的大小，单层的卷积神经网络只能捕捉序列局部的相对位置信息。虽然多层的卷积神经网络可以扩大感受野，但是对全局的位置表示并不充分。而相较于基于卷积神经网络的模型，基于循环神经网络的模型按时间步对输入的序列进行建模，这样间接的对位置信息进行了建模。而词序又是自然语言处理任务中重要信息，因此这里需要单独考虑。
......@@ -257,7 +258,7 @@
\parinterval 单层卷积神经网络的感受野受限于卷积核的大小，因此只能捕捉序列中局部的上下文信息，不能很好地进行序列建模。为了捕捉更长的长下文信息，最简单的做法就是堆叠多个卷积层。相比于循环神经网络的链式结构，对相同的上下文跨度，多层卷积神经网络的层级结构可以通过更少的非线性计算对其进行建模，缓解了长距离建模中的梯度消失问题。因此，卷积神经网络相对更容易进行训练优化。
\parinterval 在ConvS2S模型中，编码端和解码端分别使用堆叠的门控卷积网络对源语和目标语序列进行建模，在传统卷积网络的基础上引入了门控线性单元（Gated Linear Units，GLU）\upcite{Dauphin2017LanguageMW}，通过门控机制对卷积输出进行控制，它在模型中的位置如图\ref{fig:11-13}黄色色方框所示：
\parinterval 在ConvS2S模型中，编码端和解码端分别使用堆叠的门控卷积网络对源语和目标语序列进行建模，在传统卷积网络的基础上引入了门控线性单元（Gated Linear Units，GLU）\upcite{Dauphin2017LanguageMW}，通过门控机制对卷积输出进行控制，它在模型中的位置如图\ref{fig:11-13}黄色方框所示：
%----------------------------------------------
% 图13.
......@@ -296,7 +297,7 @@
\noindent 其中$\sigma$为sigmoid函数，$\otimes$为按位乘运算。sigmoid将$\mathbi{B}$映射为0-1范围内的实数，用来充当门控。可以看到，门控卷积网络中核心部分就是$\sigma ( \mathbi{B} )$，通过这个门控单元来对卷积输出进行控制，确定保留哪些信息。同时，在梯度反向传播的过程中，这种机制使得不同层之间存在线性的通道，梯度传导更加简单，利于深层网络的训练。这种思想和下一节介绍的残差网络也很类似。
\parinterval 在ConvS2S模型中，为了保证卷积操作之后的序列长度不变，需要对输入进行填充，这一点已经在之前的章节中讨论过了。因此，在编码端每一次卷积操作前，需要对序列的头部和尾部分别做相应的填充（如图\ref{fig:11-14}输入部分）。而在解码端中，由于需要训练和解码的一致性，模型在训练过程中不能使用未来的信息，需要对未来信息进行屏蔽。从实践角度来看，只需要对解码端输入序列的头部填充$k-1$个空元素，其中$k$为卷积核的宽度（如图\ref{fig:11-14-2}所示）。
\parinterval 在ConvS2S模型中，为了保证卷积操作之后的序列长度不变，需要对输入进行填充，这一点已经在之前的章节中讨论过了。因此，在编码端每一次卷积操作前，需要对序列的头部和尾部分别做相应的填充（如图\ref{fig:11-14}输入部分）。而在解码端中，由于需要训练和解码的一致性，模型在训练过程中不能使用未来的信息，需要对未来信息进行屏蔽。从实践角度来看，只需要对解码端输入序列的头部填充$K-1$个空元素，其中$K$为卷积核的宽度（图\ref{fig:11-14-2}展示了卷积核宽度$K$=3时，解码端对输入序列的填充情况）。
%----------------------------------------------
% 图14-2.
......@@ -333,7 +334,7 @@
\label{eq:11-3}
\end{eqnarray}
\noindent 其中，$\mathbi{h}^l$表示$l$层网络的输入向量，${F} (\mathbi{h}^l)$是子层运算。如果$l=2$，那么公式\eqref{eq:11-3}可以解释为，第3层的输入（$\mathbi{h}^3$）等于第2层的输出（${F}(\mathbi{h}^2)$）加上第二层的输入（$\mathbi{h}^2$）。
\noindent 其中，$\mathbi{h}^l$表示$l$层网络的输入向量，${F} (\mathbi{h}^l)$是子层运算。如果$l=2$，那么公式\eqref{eq:11-3}可以解释为，第3层的输入$\mathbi{h}^3$等于第2层的输出${F}(\mathbi{h}^2)$加上第二层的输入$\mathbi{h}^2$。
\parinterval 在ConvS2S中残差连接主要应用于门控卷积网络和多跳自注意力机制中，比如在多层的门控卷积网络中，在每一层的输入和输出之间增加残差连接，具体的数学描述如下：
\begin{eqnarray}
......@@ -364,7 +365,7 @@
\parinterval 注意力机制早在基于循环神经网络的翻译模型中被广泛使用\upcite{bahdanau2014neural}，用于避免循环神经网络将源语言序列压缩成一个固定维度的向量表示带来的信息损失。另一方面，注意力同样能够帮助解码端区分源语言中不同位置词对当前解码词的贡献权重，其具体的计算过程如下：
\begin{eqnarray}
\mathbi{C}_j &=& \sum_i \alpha_{i,j} \mathbi{h}_i \\
\alpha_{i,j} &=& \frac{ \textrm{exp}(a (\mathbi{s}_{j-1},\mathbi{h}_i)) }{\sum_{i'} \textrm{exp}(a(\mathbi{s}_{j-1},\mathbi{h}_{i'}))}
\alpha_{i,j} &=& \frac{ \textrm{exp}(\textrm{a} (\mathbi{s}_{j-1},\mathbi{h}_i)) }{\sum_{i'} \textrm{exp}( \textrm{a} (\mathbi{s}_{j-1},\mathbi{h}_{i'}))}
\label{eq:11-5}
\end{eqnarray}
......@@ -523,7 +524,7 @@
\parinterval 在序列建模的模型中，一个很重要的模块就是对序列中不同位置信息的提取，如ConvS2S中的卷积网络等。虽然考虑局部上下文的卷积神经网络只在序列这一维度进行操作，具有线性的复杂度，但是由于标准卷积操作中考虑了不同通道的信息交互，整体复杂度依旧较高。一种简化的策略就是采取通道独立的卷积操作，也就是\ref{sec:11.3.1}节中介绍的深度卷积。
\parinterval 在神经机器翻译模型中，多层表示的维度通常一致，即$O=N=d$。因此，深度卷积可以使得卷积网络参数量从 $Kd^2$ 降到$Kd$（参考表\ref{tab:11-1}）。从形式上来看，深度卷积和注意力很类似，区别在于注意力机制考虑了序列全局上下文信息，权重来自于当前位置对其他位置的“注意力”，而深度卷积中仅考虑了局部的上下文信息，权重采用了在不同通道上独立的固定参数。为了进一步降低参数量，轻量卷积共享了部分通道的卷积参数。如图\ref{fig:11-18}所示，深度卷积中4种颜色的连接代表了4个通道上独立的卷积核，而轻量卷积中，第一和第三通道，第二和第四通道采用了共享的卷积核参数。通过共享，可以将参数量压缩到$Ka$，其中压缩比例为$d/a$（$a$压缩后保留的共享通道数）。
\parinterval 在神经机器翻译模型中，多层表示的维度通常一致，即$O=N=d$。因此，深度卷积可以使得卷积网络参数量从 $Kd^2$ 降到$Kd$（参考表\ref{tab:11-1}）。从形式上来看，深度卷积和注意力很类似，区别在于注意力机制考虑了序列全局上下文信息，权重来自于当前位置对其他位置的“注意力”，而深度卷积中仅考虑了局部的上下文信息，权重采用了在不同通道上独立的固定参数。为了进一步降低参数量，轻量卷积共享了部分通道的卷积参数。如图\ref{fig:11-18}所示，深度卷积中4种颜色的连接代表了4个通道上独立的卷积核，而轻量卷积中，第一和第三通道，第二和第四通道采用了共享的卷积核参数。通过共享，可以将参数量压缩到$Ka$，其中压缩比例为$d/a$（$a$为压缩后保留的共享通道数）。
%----------------------------------------------
% 图18.
......@@ -562,11 +563,11 @@
% NEW SUB-SECTION
%----------------------------------------------------------------------------------------
\subsection{小节及拓展阅读}
\section{小节及拓展阅读}
\parinterval 卷积是一种高效处理网格数据的计算方式，在图像、语音等领域取得了令人瞩目的成绩。本章介绍了卷积的概念及其特性，并对池化、填充等操作进行了详细的讨论。前面介绍的基于循环神经网络的翻译模型在引入注意力机制后已经大幅度超越了基于统计的机器翻译模型，但由于循环神经网络的计算方式导致网络整体的并行能力差，训练耗时。本章介绍了具有高并行计算的能力的模型范式，即基于卷积神经网络的编码器-解码器框架。其在机器翻译任务上取得了与基于循环神经网络的GNMT模型相当的性能，并大幅度缩短了模型的训练周期。除了基础部分，本章还针对卷积计算进行了延伸，包括逐通道卷积、逐点卷积、轻量卷积和动态卷积等。除了上述提及的内容，卷积神经网络及其变种在文本分类、命名实体识别等其他自然语言处理任务上也有许多应用。
\parinterval 和机器翻译任务不同的是，文本分类任务侧重于对序列特征的提取，然后通过压缩后的特征表示做出类别预测。卷积神经网络可以对序列中一些$n$-gram特征进行提取，也可以用在文本分类任务中，其基本结构包括输入层、卷积层、池化层和全连接层。除了在本章介绍过的TextCNN模型\upcite{Kim2014ConvolutionalNN}，不少研究工作在此基础上对其进行改进。比如，通过改变输入层来引入更多特征\upcite{DBLP:conf/acl/NguyenG15,DBLP:conf/aaai/LaiXLZ15}，对卷积层的改进\upcite{DBLP:conf/acl/ChenXLZ015,DBLP:conf/emnlp/LeiBJ15}以及对池化层的改进\upcite{Kalchbrenner2014ACN,DBLP:conf/acl/ChenXLZ015}。在命名实体识别任务中，同样可以使用卷积神经网络来进行特征提取\upcite{DBLP:journals/jmlr/CollobertWBKKK11,DBLP:conf/cncl/ZhouZXQBX17}，或者使用更高效的空洞卷积对更长的上下文进行建模\upcite{DBLP:conf/emnlp/StrubellVBM17}。此外，也有一些研究工作尝试使用卷积神经网络来提取字符级特征\upcite{DBLP:conf/acl/MaH16,DBLP:conf/emnlp/LiDWCM17,DBLP:conf/acl-codeswitch/WangCK18}。
\parinterval 和机器翻译任务不同的是，文本分类任务侧重于对序列特征的提取，然后通过压缩后的特征表示做出类别预测。卷积神经网络可以对序列中一些$n$-gram特征进行提取，也可以用在文本分类任务中，其基本结构包括输入层、卷积层、池化层和全连接层。除了在本章介绍过的TextCNN模型\upcite{Kim2014ConvolutionalNN}，不少研究工作在此基础上对其进行改进。比如，通过改变输入层来引入更多特征\upcite{DBLP:conf/acl/NguyenG15,DBLP:conf/aaai/LaiXLZ15}，对卷积层的改进\upcite{DBLP:conf/acl/ChenXLZ015,DBLP:conf/emnlp/LeiBJ15}以及对池化层的改进\upcite{Kalchbrenner2014ACN,DBLP:conf/acl/ChenXLZ015}。在命名实体识别任务中，同样可以使用卷积神经网络来进行特征提取\upcite{2011Natural,DBLP:conf/cncl/ZhouZXQBX17}，或者使用更高效的空洞卷积对更长的上下文进行建模\upcite{DBLP:conf/emnlp/StrubellVBM17}。此外，也有一些研究工作尝试使用卷积神经网络来提取字符级特征\upcite{DBLP:conf/acl/MaH16,DBLP:conf/emnlp/LiDWCM17,DBLP:conf/acl-codeswitch/WangCK18}。
......
Chapter16/Figures/figure-three-common-methods-of-adding-noise.tex
......@@ -138,7 +138,7 @@
\node [anchor=south,inner sep=2pt,minimum height=1.5em,minimum width=3.0em] (c10) at (c11.north) {\scriptsize{源语言}};
\node [anchor=south,inner sep=2pt,minimum height=1.5em,minimum width=3.0em] (c30) at (c31.north) {\small{$n$=3}};
\node [anchor=south,inner sep=2pt,minimum height=1.5em,minimum width=3.0em] (c50) at (c51.north) {\small{$S$}};
\node [anchor=south,inner sep=2pt,minimum height=1.5em,minimum width=3.0em] (c50) at (c51.north) {\small{$\mathbi{S}$}};
\node [anchor=south,inner sep=2pt] (c60) at (c61.north) {\scriptsize{进行排序}};
\node [anchor=south,inner sep=2pt] (c60-2) at (c60.north) {\scriptsize{由小到大}};
......
Chapter16/chapter16.tex
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Chapter9/chapter9.tex
......@@ -2166,6 +2166,6 @@ Jobs was the CEO of {\red{\underline{apple}}}.
\vspace{0.5em}
\item 为了进一步提高神经语言模型性能，除了改进模型，还可以在模型中引入新的结构或是其他有效信息，该领域也有很多典型工作值得关注。例如在神经语言模型中引入除了词嵌入以外的单词特征，如语言特征（形态、语法、语义特征等）\upcite{Wu2012FactoredLM,Adel2015SyntacticAS}、上下文信息\upcite{mikolov2012context,Wang2015LargerContextLM}、知识图谱等外部知识\upcite{Ahn2016ANK}；或是在神经语言模型中引入字符级信息，将其作为字符特征单独\upcite{Kim2016CharacterAwareNL,Hwang2017CharacterlevelLM}或与单词特征一起\upcite{Onoe2016GatedWR,Verwimp2017CharacterWordLL}送入模型中；在神经语言模型中引入双向模型也是一种十分有效的尝试，在单词预测时可以同时利用来自过去和未来的文本信息\upcite{Graves2013HybridSR,bahdanau2014neural,Peters2018DeepCW}。
\vspace{0.5em}
\item 词嵌入是自然语言处理近些年的重要进展。所谓“嵌入”是一类方法，理论上，把一个事物进行分布式表示的过程都可以被看作是广义上的“嵌入”。基于这种思想的表示学习也成为了自然语言处理中的前沿方法。比如，如何对树结构，甚至图结构进行分布式表示成为了分析自然语言的重要方法\upcite{DBLP:journals/corr/abs-1809-01854,Yin2018StructVAETL,Aharoni2017TowardsSN,Bastings2017GraphCE,KoncelKedziorski2019TextGF}。此外，除了语言建模，还有很多方式可以进行词嵌入的学习，比如，SENNA\upcite{collobert2011natural}、word2vec\upcite{DBLP:journals/corr/abs-1301-3781,mikolov2013distributed}、Glove\upcite{DBLP:conf/emnlp/PenningtonSM14}、CoVe\upcite{mccann2017learned} 等。
\item 词嵌入是自然语言处理近些年的重要进展。所谓“嵌入”是一类方法，理论上，把一个事物进行分布式表示的过程都可以被看作是广义上的“嵌入”。基于这种思想的表示学习也成为了自然语言处理中的前沿方法。比如，如何对树结构，甚至图结构进行分布式表示成为了分析自然语言的重要方法\upcite{DBLP:journals/corr/abs-1809-01854,Yin2018StructVAETL,Aharoni2017TowardsSN,Bastings2017GraphCE,KoncelKedziorski2019TextGF}。此外，除了语言建模，还有很多方式可以进行词嵌入的学习，比如，SENNA\upcite{2011Natural}、word2vec\upcite{DBLP:journals/corr/abs-1301-3781,mikolov2013distributed}、Glove\upcite{DBLP:conf/emnlp/PenningtonSM14}、CoVe\upcite{mccann2017learned} 等。
\vspace{0.5em}
\end{itemize}
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%%%%%%%%%%%%%%%%%%%%%%%神经语言模型，待检查修改%%%%%%%%%%%%%%%%%%%%%%%%%
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author = {Tong Xiao and
......@@ -4556,12 +4519,9 @@ author = {Yoshua Bengio and
Tongran Liu and
Chunliang Zhang},
title = {Fast Parallel Training of Neural Language Models},
publisher = {Proceedings of the Twenty-Sixth International Joint Conference on
Artificial Intelligence, {IJCAI} 2017, Melbourne, Australia, August
19-25, 2017},
publisher = {International Joint Conference on Artificial Intelligence},
pages = {4193--4199},
//publisher = {ijcai.org},
year = {2017},
year = {2017}
}
@inproceedings{Gu2017NonAutoregressiveNM,
author = {Jiatao Gu and
......@@ -4571,7 +4531,7 @@ author = {Yoshua Bengio and
Richard Socher},
title = {Non-Autoregressive Neural Machine Translation},
publisher = {International Conference on Learning Representations},
year = {2018},
year = {2018}
}
@inproceedings{li-etal-2018-simple,
author = {Yanyang Li and
......@@ -4581,12 +4541,9 @@ author = {Yoshua Bengio and
Changming Xu and
Jingbo Zhu},
title = {A Simple and Effective Approach to Coverage-Aware Neural Machine Translation},
publisher = {Proceedings of the 56th Annual Meeting of the Association for Computational
Linguistics, {ACL} 2018, Melbourne, Australia, July 15-20, 2018, Volume
2: Short Papers},
publisher = {Annual Meeting of the Association for Computational Linguistics},
pages = {292--297},
//publisher = {Association for Computational Linguistics},
year = {2018},
year = {2018}
}
@inproceedings{TuModeling,
author = {Zhaopeng Tu and
......@@ -4595,11 +4552,8 @@ author = {Yoshua Bengio and
Xiaohua Liu and
Hang Li},
title = {Modeling Coverage for Neural Machine Translation},
publisher = {Proceedings of the 54th Annual Meeting of the Association for Computational
Linguistics, {ACL} 2016, August 7-12, 2016, Berlin, Germany, Volume
1: Long Papers},
//publisher = {The Association for Computer Linguistics},
year = {2016},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2016}
}
@inproceedings{DBLP:journals/corr/SennrichFCBHHJL17,
author = {Rico Sennrich and
......@@ -4614,23 +4568,17 @@ author = {Yoshua Bengio and
Jozef Mokry and
Maria Nadejde},
title = {Nematus: a Toolkit for Neural Machine Translation},
publisher = {Proceedings of the 15th Conference of the European Chapter of the
Association for Computational Linguistics, {EACL} 2017, Valencia,
Spain, April 3-7, 2017, Software Demonstrations},
publisher = {European Association of Computational Linguistics},
pages = {65--68},
//publisher = {Association for Computational Linguistics},
year = {2017},
year = {2017}
}
@inproceedings{DBLP:journals/corr/abs-1905-13324,
author = {Biao Zhang and
Rico Sennrich},
title = {A Lightweight Recurrent Network for Sequence Modeling},
publisher = {Proceedings of the 57th Conference of the Association for Computational
Linguistics, {ACL} 2019, Florence, Italy, July 28- August 2, 2019,
Volume 1: Long Papers},
publisher = {Annual Meeting of the Association for Computational Linguistics},
pages = {1538--1548},
//publisher = {Association for Computational Linguistics},
year = {2019},
year = {2019}
}
@article{Lei2017TrainingRA,
author = {Tao Lei and
......@@ -4639,7 +4587,7 @@ author = {Yoshua Bengio and
title = {Training RNNs as Fast as CNNs},
journal = {CoRR},
volume = {abs/1709.02755},
year = {2017},
year = {2017}
}
@inproceedings{Zhang2018SimplifyingNM,
author = {Biao Zhang and
......@@ -4649,22 +4597,18 @@ author = {Yoshua Bengio and
Huiji Zhang},
title = {Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated
Recurrent Networks},
publisher = {Proceedings of the 2018 Conference on Empirical Methods in Natural
Language Processing, Brussels, Belgium, October 31 - November 4, 2018},
publisher = {Conference on Empirical Methods in Natural Language Processing},
pages = {4273--4283},
//publisher = {Association for Computational Linguistics},
year = {2018},
year = {2018}
}
@inproceedings{Liu_2019_CVPR,
author = {Shikun Liu and
Edward Johns and
Andrew J. Davison},
title = {End-To-End Multi-Task Learning With Attention},
publisher = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
2019, Long Beach, CA, USA, June 16-20, 2019},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition},
pages = {1871--1880},
//publisher = {Computer Vision Foundation / {IEEE}},
year = {2019},
year = {2019}
}
@inproceedings{DBLP:journals/corr/abs-1811-00498,
author = {Ra{\'{u}}l V{\'{a}}zquez and
......@@ -4672,11 +4616,9 @@ author = {Yoshua Bengio and
J{\"{o}}rg Tiedemann and
Mathias Creutz},
title = {Multilingual {NMT} with a Language-Independent Attention Bridge},
publisher = {Proceedings of the 4th Workshop on Representation Learning for NLP,
RepL4NLP@ACL 2019, Florence, Italy, August 2, 2019},
publisher = {Annual Meeting of the Association for Computational Linguistics},
pages = {33--39},
//publisher = {Association for Computational Linguistics},
year = {2019},
year = {2019}
}
@inproceedings{MoradiInterrogating,
author = {Pooya Moradi and
......@@ -4684,11 +4626,9 @@ author = {Yoshua Bengio and
Anoop Sarkar},
title = {Interrogating the Explanatory Power of Attention in Neural Machine
Translation},
publisher = {Proceedings of the 3rd Workshop on Neural Generation and Translation@EMNLP-IJCNLP
2019, Hong Kong, November 4, 2019},
publisher = {Conference on Empirical Methods in Natural Language Processing},
pages = {221--230},
//publisher = {Association for Computational Linguistics},
year = {2019},
year = {2019}
}
@inproceedings{WangNeural,
author = {Xing Wang and
......@@ -4698,11 +4638,9 @@ author = {Yoshua Bengio and
Deyi Xiong and
Min Zhang},
title = {Neural Machine Translation Advised by Statistical Machine Translation},
publisher = {Proceedings of the Thirty-First {AAAI} Conference on Artificial Intelligence,
February 4-9, 2017, San Francisco, California, {USA}},
publisher = {AAAI Conference on Artificial Intelligence},
pages = {3330--3336},
//publisher = {{AAAI} Press},
year = {2017},
year = {2017}
}
@inproceedings{Xiao2019SharingAW,
author = {Tong Xiao and
......@@ -4711,12 +4649,9 @@ author = {Yoshua Bengio and
Zhengtao Yu and
Tongran Liu},
title = {Sharing Attention Weights for Fast Transformer},
publisher = {Proceedings of the Twenty-Eighth International Joint Conference on
Artificial Intelligence, {IJCAI} 2019, Macao, China, August 10-16,
2019},
publisher = {International Joint Conference on Artificial Intelligence},
pages = {5292--5298},
//publisher = {ijcai.org},
year = {2019},
year = {2019}
}
@inproceedings{Yang2017TowardsBH,
author = {Baosong Yang and
......@@ -4726,36 +4661,27 @@ author = {Yoshua Bengio and
Jingbo Zhu},
title = {Towards Bidirectional Hierarchical Representations for Attention-based
Neural Machine Translation},
publisher = {Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing, {EMNLP} 2017, Copenhagen, Denmark, September
9-11, 2017},
publisher = {Conference on Empirical Methods in Natural Language Processing},
pages = {1432--1441},
//publisher = {Association for Computational Linguistics},
year = {2017},
year = {2017}
}
@inproceedings{Wang2019TreeTI,
author = {Yau-Shian Wang and
Hung-yi Lee and
Yun-Nung Chen},
title = {Tree Transformer: Integrating Tree Structures into Self-Attention},
publisher = {Proceedings of the 2019 Conference on Empirical Methods in Natural
Language Processing and the 9th International Joint Conference on
Natural Language Processing, {EMNLP-IJCNLP} 2019, Hong Kong, China,
November 3-7, 2019},
//publisher = {Association for Computational Linguistics},
publisher = {Conference on Empirical Methods in Natural Language Processing},
pages = {1061--1070},
year = {2019},
year = {2019}
}
@inproceedings{DBLP:journals/corr/abs-1809-01854,
author = {Jetic Gu and
Hassan S. Shavarani and
Anoop Sarkar},
title = {Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing},
publisher = {Proceedings of the 2018 Conference on Empirical Methods in Natural
Language Processing, Brussels, Belgium, October 31 - November 4, 2018},
publisher = {Conference on Empirical Methods in Natural Language Processing},
pages = {401--413},
//publisher = {Association for Computational Linguistics},
year = {2018},
year = {2018}
}
@inproceedings{DBLP:journals/corr/abs-1808-09374,
author = {Xinyi Wang and
......@@ -4763,11 +4689,9 @@ author = {Yoshua Bengio and
Pengcheng Yin and
Graham Neubig},
title = {A Tree-based Decoder for Neural Machine Translation},
publisher = {Proceedings of the 2018 Conference on Empirical Methods in Natural
Language Processing, Brussels, Belgium, October 31 - November 4, 2018},
publisher = {Conference on Empirical Methods in Natural Language Processing},
pages = {4772--4777},
//publisher = {Association for Computational Linguistics},
year = {2018},
year = {2018}
}
@article{DBLP:journals/corr/ZhangZ16c,
author = {Jiajun Zhang and
......@@ -4775,7 +4699,7 @@ author = {Yoshua Bengio and
title = {Bridging Neural Machine Translation and Bilingual Dictionaries},
journal = {CoRR},
volume = {abs/1610.07272},
year = {2016},
year = {2016}
}
@article{Dai2019TransformerXLAL,
author = {Zihang Dai and
......@@ -4787,7 +4711,7 @@ author = {Yoshua Bengio and
title = {Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context},
journal = {CoRR},
volume = {abs/1901.02860},
year = {2019},
year = {2019}
}
@inproceedings{li-etal-2019-word,
author = {Xintong Li and
......@@ -4796,12 +4720,9 @@ author = {Yoshua Bengio and
Max Meng and
Shuming Shi},
title = {On the Word Alignment from Neural Machine Translation},
publisher = {Proceedings of the 57th Conference of the Association for Computational
Linguistics, {ACL} 2019, Florence, Italy, July 28- August 2, 2019,
Volume 1: Long Papers},
publisher = {Annual Meeting of the Association for Computational Linguistics},
pages = {1293--1303},
//publisher = {Association for Computational Linguistics},
year = {2019},
year = {2019}
}
@inproceedings{Werlen2018DocumentLevelNM,
......@@ -4811,11 +4732,9 @@ author = {Yoshua Bengio and
James Henderson},
title = {Document-Level Neural Machine Translation with Hierarchical Attention
Networks},
publisher = {Proceedings of the 2018 Conference on Empirical Methods in Natural
Language Processing, Brussels, Belgium, October 31 - November 4, 2018},
publisher = {Conference on Empirical Methods in Natural Language Processing},
pages = {2947--2954},
//publisher = {Association for Computational Linguistics},
year = {2018},
year = {2018}
}
@inproceedings{DBLP:journals/corr/abs-1805-10163,
author = {Elena Voita and
......@@ -4823,12 +4742,9 @@ author = {Yoshua Bengio and
Rico Sennrich and
Ivan Titov},
title = {Context-Aware Neural Machine Translation Learns Anaphora Resolution},
publisher = {Proceedings of the 56th Annual Meeting of the Association for Computational
Linguistics, {ACL} 2018, Melbourne, Australia, July 15-20, 2018, Volume
1: Long Papers},
publisher = {Annual Meeting of the Association for Computational Linguistics},
pages = {1264--1274},
//publisher = {Association for Computational Linguistics},
year = {2018},
year = {2018}
}
@article{DBLP:journals/corr/abs-1906-00532,
author = {Aishwarya Bhandare and
......@@ -4842,7 +4758,7 @@ author = {Yoshua Bengio and
Translation Model},
journal = {CoRR},
volume = {abs/1906.00532},
year = {2019},
year = {2019}
}
@inproceedings{Zhang2018SpeedingUN,
......@@ -4852,22 +4768,18 @@ author = {Yoshua Bengio and
Lei Shen and
Qun Liu},
title = {Speeding Up Neural Machine Translation Decoding by Cube Pruning},
publisher = {Proceedings of the 2018 Conference on Empirical Methods in Natural
Language Processing, Brussels, Belgium, October 31 - November 4, 2018},
publisher = {Conference on Empirical Methods in Natural Language Processing},
pages = {4284--4294},
//publisher = {Association for Computational Linguistics},
year = {2018},
year = {2018}
}
@inproceedings{DBLP:journals/corr/SeeLM16,
author = {Abigail See and
Minh-Thang Luong and
Christopher D. Manning},
title = {Compression of Neural Machine Translation Models via Pruning},
publisher = {Proceedings of the 20th {SIGNLL} Conference on Computational Natural
Language Learning, CoNLL 2016, Berlin, Germany, August 11-12, 2016},
publisher = {International Conference on Computational Linguistics},
pages = {291--301},
//publisher = {{ACL}},
year = {2016},
year = {2016}
}
@inproceedings{DBLP:journals/corr/ChenLCL17,
author = {Yun Chen and
......@@ -4875,12 +4787,9 @@ author = {Yoshua Bengio and
Yong Cheng and
Victor O. K. Li},
title = {A Teacher-Student Framework for Zero-Resource Neural Machine Translation},
publisher = {Proceedings of the 55th Annual Meeting of the Association for Computational
Linguistics, {ACL} 2017, Vancouver, Canada, July 30 - August 4, Volume
1: Long Papers},
pages = {1925--1935},
//publisher = {Association for Computational Linguistics},
year = {2017},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2017}
}
@article{Hinton2015Distilling,
author = {Geoffrey E. Hinton and
......@@ -4889,13 +4798,13 @@ author = {Yoshua Bengio and
title = {Distilling the Knowledge in a Neural Network},
journal = {CoRR},
volume = {abs/1503.02531},
year = {2015},
year = {2015}
}
@inproceedings{Ott2018ScalingNM,
title={Scaling Neural Machine Translation},
author={Myle Ott and Sergey Edunov and David Grangier and M. Auli},
publisher={Workshop on Machine Translation},
publisher={Annual Meeting of the Association for Computational Linguistics},
year={2018}
}
@inproceedings{Lin2020TowardsF8,
......@@ -4915,7 +4824,7 @@ author = {Yoshua Bengio and
Alexander M. Rush},
title = {Sequence-Level Knowledge Distillation},
pages = {1317--1327},
publisher = {The Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2016}
}
@article{Akaike1969autoregressive,
......@@ -4946,13 +4855,13 @@ author = {Yoshua Bengio and
title = {The Best of Both Worlds: Combining Recent Advances in Neural Machine
Translation},
pages = {76--86},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
@inproceedings{He2018LayerWiseCB,
title={Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation},
author={Tianyu He and X. Tan and Yingce Xia and D. He and T. Qin and Zhibo Chen and T. Liu},
publisher={Conference and Workshop on Neural Information Processing Systems},
publisher={Conference on Neural Information Processing Systems},
year={2018}
}
@inproceedings{cho-etal-2014-properties,
......@@ -4962,7 +4871,7 @@ author = {Yoshua Bengio and
Yoshua Bengio},
title = {On the Properties of Neural Machine Translation: Encoder-Decoder Approaches},
pages = {103--111},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2014}
}
......@@ -4973,7 +4882,7 @@ author = {Yoshua Bengio and
Yoshua Bengio},
title = {On Using Very Large Target Vocabulary for Neural Machine Translation},
pages = {1--10},
publisher = {The Association for Computer Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2015}
}
......@@ -4982,8 +4891,7 @@ author = {Yoshua Bengio and
Hieu Pham and
Christopher D. Manning},
title = {Effective Approaches to Attention-based Neural Machine Translation},
publisher = {Conference on Empirical Methods in Natural
Language Processing},
publisher = {Conference on Empirical Methods in Natural Language Processing},
pages = {1412--1421},
year = {2015}
}
......@@ -4994,7 +4902,7 @@ author = {Yoshua Bengio and
Haifeng Wang},
title = {Improved Neural Machine Translation with {SMT} Features},
pages = {151--157},
publisher = {the Association for the Advance of Artificial Intelligence},
publisher = {AAAI Conference on Artificial Intelligence},
year = {2016}
}
@inproceedings{zhang-etal-2017-prior,
......@@ -5005,7 +4913,7 @@ author = {Yoshua Bengio and
Xu, Jingfang and
Sun, Maosong},
year = {2017},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
pages = {1514--1523},
}
......@@ -5021,7 +4929,7 @@ author = {Yoshua Bengio and
title = {Bilingual Dictionary Based Neural Machine Translation without Using
Parallel Sentences},
pages = {1570--1579},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2020}
}
......@@ -5030,7 +4938,7 @@ author = {Yoshua Bengio and
Deyi Xiong},
title = {Encoding Gated Translation Memory into Neural Machine Translation},
pages = {3042--3047},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
@inproceedings{yang-etal-2016-hierarchical,
......@@ -5042,7 +4950,7 @@ author = {Yoshua Bengio and
Eduard H. Hovy},
title = {Hierarchical Attention Networks for Document Classification},
pages = {1480--1489},
publisher = {The Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2016}
}
%%%%% chapter 10------------------------------------------------------
......@@ -5056,7 +4964,7 @@ author = {Yoshua Bengio and
Douwe Kiela},
title = {Code-Switched Named Entity Recognition with Embedding Attention},
pages = {154--158},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
......@@ -5069,7 +4977,7 @@ author = {Yoshua Bengio and
title = {Leveraging Linguistic Structures for Named Entity Recognition with
Bidirectional Recursive Neural Networks},
pages = {2664--2669},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2017}
}
......@@ -5077,7 +4985,7 @@ author = {Yoshua Bengio and
author = {Xuezhe Ma and
Eduard H. Hovy},
title = {End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF},
publisher = {The Association for Computer Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2016}
}
......@@ -5088,7 +4996,7 @@ author = {Yoshua Bengio and
Andrew McCallum},
title = {Fast and Accurate Entity Recognition with Iterated Dilated Convolutions},
pages = {2670--2680},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2017}
}
......@@ -5107,26 +5015,21 @@ author = {Yoshua Bengio and
year = {2017}
}
@article{DBLP:journals/jmlr/CollobertWBKKK11,
author = {Ronan Collobert and
Jason Weston and
L{\'{e}}on Bottou and
Michael Karlen and
Koray Kavukcuoglu and
Pavel P. Kuksa},
title = {Natural Language Processing (Almost) from Scratch},
journal = {J. Mach. Learn. Res.},
volume = {12},
pages = {2493--2537},
year = {2011}
@article{2011Natural,
title={Natural Language Processing (almost) from Scratch},
author={ Collobert, Ronan and Weston, Jason and Bottou, Léon and Karlen, Michael and Kavukcuoglu, Koray and Kuksa, Pavel },
journal={Journal of Machine Learning Research},
volume={12},
number={1},
pages={2493-2537},
year={2011},
}
@inproceedings{DBLP:conf/acl/NguyenG15,
author = {Thien Huu Nguyen and
Ralph Grishman},
title = {Event Detection and Domain Adaptation with Convolutional Neural Networks},
pages = {365--371},
publisher = {The Association for Computer Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2015}
}
......@@ -5137,7 +5040,7 @@ author = {Yoshua Bengio and
Jun Zhao},
title = {Recurrent Convolutional Neural Networks for Text Classification},
pages = {2267--2273},
publisher = {the Association for the Advance of Artificial Intelligence},
publisher = {AAAI Conference on Artificial Intelligence},
year = {2015}
}
......@@ -5149,7 +5052,7 @@ author = {Yoshua Bengio and
Jun Zhao},
title = {Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks},
pages = {167--176},
publisher = {The Association for Computer Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2015}
}
......@@ -5159,7 +5062,7 @@ author = {Yoshua Bengio and
Tommi S. Jaakkola},
title = {Molding CNNs for text: non-linear, non-consecutive convolutions},
pages = {1565--1575},
publisher = {The Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2015}
}
......@@ -5169,7 +5072,7 @@ author = {Yoshua Bengio and
title = {Effective Use of Word Order for Text Categorization with Convolutional
Neural Networks},
pages = {103--112},
publisher = {The Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2015}
}
......@@ -5178,14 +5081,14 @@ author = {Yoshua Bengio and
Ralph Grishman},
title = {Relation Extraction: Perspective from Convolutional Neural Networks},
pages = {39--48},
publisher = {The Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2015}
}
@article{StahlbergNeural,
title={Neural Machine Translation: A Review},
author={Felix Stahlberg},
journal={journal of artificial intelligence research},
journal={Journal of Artificial Intelligence Research},
year={2020},
volume={69},
pages={343-418}
......@@ -5211,7 +5114,7 @@ author = {Yoshua Bengio and
@article{Waibel1989PhonemeRU,
title={Phoneme recognition using time-delay neural networks},
author={Alexander H. Waibel and Toshiyuki Hanazawa and Geoffrey E. Hinton and K. Shikano and K. Lang},
journal={IEEE Trans. Acoust. Speech Signal Process.},
journal={IEEE Transactions on Acoustics, Speech, and Signal Processing},
year={1989},
volume={37},
pages={328-339}
......@@ -5226,7 +5129,7 @@ author = {Yoshua Bengio and
pages={541-551}
}
@ARTICLE{726791,
@article{726791,
author={Y. {Lecun} and L. {Bottou} and Y. {Bengio} and P. {Haffner}},
journal={Proceedings of the IEEE},
title={Gradient-based learning applied to document recognition},
......@@ -5234,7 +5137,6 @@ author = {Yoshua Bengio and
volume={86},
number={11},
pages={2278-2324},
//doi={10.1109/5.726791}
}
@inproceedings{DBLP:journals/corr/HeZRS15,
......@@ -5262,7 +5164,7 @@ author = {Yoshua Bengio and
@article{Girshick2015FastR,
title={Fast R-CNN},
author={Ross B. Girshick},
journal={2015 IEEE International Conference on Computer Vision (ICCV)},
journal={International Conference on Computer Vision},
year={2015},
pages={1440-1448}
}
......@@ -5279,7 +5181,7 @@ author = {Yoshua Bengio and
@inproceedings{Kalchbrenner2014ACN,
title={A Convolutional Neural Network for Modelling Sentences},
author={Nal Kalchbrenner and Edward Grefenstette and P. Blunsom},
booktitle={ACL},
publisher={Annual Meeting of the Association for Computational Linguistics},
pages={655--665},
year={2014}
}
......@@ -5287,7 +5189,7 @@ author = {Yoshua Bengio and
@inproceedings{Kim2014ConvolutionalNN,
title={Convolutional Neural Networks for Sentence Classification},
author={Yoon Kim},
booktitle={Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing},
publisher={Conference on Empirical Methods in Natural Language Processing},
pages = {1746--1751},
year={2014}
}
......@@ -5299,7 +5201,7 @@ author = {Yoshua Bengio and
Bowen Zhou and
Bing Xiang},
pages = {174--179},
booktitle={The Association for Computer Linguistics},
publisher={Annual Meeting of the Association for Computational Linguistics},
year={2015}
}
......@@ -5308,7 +5210,7 @@ author = {Yoshua Bengio and
author = {C{\'{\i}}cero Nogueira dos Santos and
Maira Gatti},
pages = {69--78},
publisher = {The Association for Computer Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year={2014}
}
......@@ -5318,7 +5220,7 @@ author = {Yoshua Bengio and
Angela Fan and
Michael Auli and
David Grangier},
booktitle={Proceedings of the 34th International Conference on Machine Learning},
publisher={International Conference on Machine Learning},
volume = {70},
pages = {933--941},
year={2017}
......@@ -5330,7 +5232,7 @@ author = {Yoshua Bengio and
Michael Auli and
David Grangier and
Yann N. Dauphin},
booktitle={The Association for Computer Linguistics},
publisher={Annual Meeting of the Association for Computational Linguistics},
pages = {123--135},
year={2017}
}
......@@ -5353,7 +5255,7 @@ author = {Yoshua Bengio and
author = {Lukasz Kaiser and
Aidan N. Gomez and
Fran{\c{c}}ois Chollet},
publisher = {OpenReview.net},
journal = {International Conference on Learning Representations},
year={2018},
}
......@@ -5364,7 +5266,7 @@ author = {Yoshua Bengio and
Yann N. Dauphin and
Michael Auli},
title = {Pay Less Attention with Lightweight and Dynamic Convolutions},
publisher = {7th International Conference on Learning Representations},
publisher = {International Conference on Learning Representations},
year = {2019},
}
......@@ -5421,7 +5323,7 @@ author = {Yoshua Bengio and
Shaoqing Ren and
Jian Sun},
title = {Deep Residual Learning for Image Recognition},
publisher = {{IEEE} Conference on Computer Vision and Pattern Recognition},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition},
pages = {770--778},
year = {2016},
}
......@@ -5432,26 +5334,26 @@ author = {Yoshua Bengio and
Arthur Szlam and
Jason Weston and
Rob Fergus},
booktitle={Conference and Workshop on Neural Information Processing Systems},
publisher={Conference on Neural Information Processing Systems},
pages = {2440--2448},
year={2015}
}
@article{Islam2020HowMP,
title={How Much Position Information Do Convolutional Neural Networks Encode?},
author={Md. Amirul Islam and Sen Jia and Neil D. B. Bruce},
journal={ArXiv},
year={2020},
volume={abs/2001.08248}
@inproceedings{Islam2020HowMP,
author = {Md. Amirul Islam and
Sen Jia and
Neil D. B. Bruce},
title = {How much Position Information Do Convolutional Neural Networks Encode?},
publisher = {International Conference on Learning Representations},
year = {2020},
}
@inproceedings{Sutskever2013OnTI,
title={On the importance of initialization and momentum in deep learning},
author = {Ilya Sutskever and
James Martens and
George E. Dahl and
Geoffrey E. Hinton},
booktitle={International Conference on Machine Learning},
publisher = {International Conference on Machine Learning},
pages = {1139--1147},
year={2013}
}
......@@ -5459,7 +5361,7 @@ author = {Yoshua Bengio and
@article{Bengio2013AdvancesIO,
title={Advances in optimizing recurrent networks},
author={Yoshua Bengio and Nicolas Boulanger-Lewandowski and Razvan Pascanu},
journal={2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
journal={IEEE Transactions on Acoustics, Speech, and Signal Processing},
year={2013},
pages={8624-8628}
}
......@@ -5476,7 +5378,7 @@ author = {Yoshua Bengio and
@article{Chollet2017XceptionDL,
title={Xception: Deep Learning with Depthwise Separable Convolutions},
author = {Fran{\c{c}}ois Chollet},
journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
journal={IEEE Conference on Computer Vision and Pattern Recognition},
year={2017},
pages={1800-1807}
}
......@@ -5512,7 +5414,7 @@ author = {Yoshua Bengio and
title={Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination},
author = {Laurent Sifre and
St{\'{e}}phane Mallat},
journal={2013 IEEE Conference on Computer Vision and Pattern Recognition},
journal={IEEE Conference on Computer Vision and Pattern Recognition},
year={2013},
pages={1233-1240}
}
......@@ -5520,7 +5422,7 @@ author = {Yoshua Bengio and
@article{Taigman2014DeepFaceCT,
title={DeepFace: Closing the Gap to Human-Level Performance in Face Verification},
author={Yaniv Taigman and Ming Yang and Marc'Aurelio Ranzato and Lior Wolf},
journal={2014 IEEE Conference on Computer Vision and Pattern Recognition},
journal={IEEE Conference on Computer Vision and Pattern Recognition},
year={2014},
pages={1701-1708}
}
......@@ -5533,7 +5435,7 @@ author = {Yoshua Bengio and
Mirk{\'{o}} Visontai and
Raziel Alvarez and
Carolina Parada},
booktitle={the International Speech Communication Association},
publisher={Conference of the International Speech Communication Association},
pages = {1136--1140},
year={2015}
}
......@@ -5546,7 +5448,7 @@ author = {Yoshua Bengio and
Dongdong Chen and
Lu Yuan and
Zicheng Liu},
publisher = {Institute of Electrical and Electronics Engineers},
journal = {IEEE Conference on Computer Vision and Pattern Recognition},
year={2020},
pages={11027-11036}
}
......@@ -5563,7 +5465,7 @@ author = {Yoshua Bengio and
Chloe Hillier and
Timothy P. Lillicrap},
title = {Compressive Transformers for Long-Range Sequence Modelling},
publisher = {OpenReview.net},
publisher = {International Conference on Learning Representations},
year = {2020}
}
......@@ -5597,7 +5499,7 @@ author = {Yoshua Bengio and
Yujun Lin and
Song Han},
title = {Lite Transformer with Long-Short Range Attention},
publisher = {OpenReview.net},
publisher = {International Conference on Learning Representations},
year = {2020}
}
......@@ -5610,7 +5512,7 @@ author = {Yoshua Bengio and
title = {Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy
Lifting, the Rest Can Be Pruned},
pages = {5797--5808},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2019},
}
......@@ -5623,7 +5525,7 @@ author = {Yoshua Bengio and
Bowen Zhou and
Yoshua Bengio},
title = {A Structured Self-Attentive Sentence Embedding},
publisher = {5th International Conference on Learning Representations},
publisher = {International Conference on Learning Representations},
year = {2017},
}
@inproceedings{Shaw2018SelfAttentionWR,
......@@ -5631,8 +5533,8 @@ author = {Yoshua Bengio and
Jakob Uszkoreit and
Ashish Vaswani},
title = {Self-Attention with Relative Position Representations},
publisher = {Proceedings of the 2018 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language Technologies},
publisher = {Proceedings of the Human Language Technology Conference of
the North American Chapter of the Association for Computational Linguistics},
pages = {464--468},
year = {2018},
}
......@@ -5642,7 +5544,7 @@ author = {Yoshua Bengio and
Shaoqing Ren and
Jian Sun},
title = {Deep Residual Learning for Image Recognition},
publisher = {{IEEE} Conference on Computer Vision and Pattern Recognition},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition},
pages = {770--778},
year = {2016},
}
......@@ -5661,7 +5563,7 @@ author = {Yoshua Bengio and
Jonathon Shlens and
Zbigniew Wojna},
title = {Rethinking the Inception Architecture for Computer Vision},
publisher = {{IEEE} Conference on Computer Vision and Pattern Recognition},
publisher = {IEEE Conference on Computer Vision and Pattern Recognition},
pages = {2818--2826},
year = {2016},
}
......@@ -5670,8 +5572,7 @@ author = {Yoshua Bengio and
Deyi Xiong and
Jinsong Su},
title = {Accelerating Neural Transformer via an Average Attention Network},
publisher = {Proceedings of the 56th Annual Meeting of the Association for Computational
Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
pages = {1789--1798},
year = {2018},
}
......@@ -5691,7 +5592,7 @@ author = {Yoshua Bengio and
Yann N. Dauphin and
Michael Auli},
title = {Pay Less Attention with Lightweight and Dynamic Convolutions},
publisher = {7th International Conference on Learning Representations},
publisher = {International Conference on Learning Representations},
year = {2019},
}
......@@ -5704,7 +5605,7 @@ author = {Yoshua Bengio and
Ruslan Salakhutdinov},
title = {Transformer-XL: Attentive Language Models beyond a Fixed-Length Context},
pages = {2978--2988},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2019}
}
@article{Liu2020LearningTE,
......@@ -5729,7 +5630,7 @@ author = {Yoshua Bengio and
Tong Zhang},
title = {Modeling Localness for Self-Attention Networks},
pages = {4449--4458},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
@inproceedings{DBLP:journals/corr/abs-1904-03107,
......@@ -5740,7 +5641,7 @@ author = {Yoshua Bengio and
Zhaopeng Tu},
title = {Convolutional Self-Attention Networks},
pages = {4040--4045},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2019},
}
@article{Wang2018MultilayerRF,
......@@ -5759,7 +5660,7 @@ author = {Yoshua Bengio and
title = {Training Deeper Neural Machine Translation Models with Transparent
Attention},
pages = {3028--3033},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
@inproceedings{Dou2018ExploitingDR,
......@@ -5770,7 +5671,7 @@ author = {Yoshua Bengio and
Tong Zhang},
title = {Exploiting Deep Representations for Neural Machine Translation},
pages = {4253--4262},
publisher = {Association for Computational Linguistics},
publisher = {Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
@inproceedings{Wang2019ExploitingSC,
......@@ -5789,13 +5690,13 @@ author = {Yoshua Bengio and
Tong Zhang},
title = {Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement},
pages = {86--93},
publisher = {the Association for the Advance of Artificial Intelligence},
publisher = {AAAI Conference on Artificial Intelligence},
year = {2019}
}
@inproceedings{Wei2020MultiscaleCD,
title={Multiscale Collaborative Deep Models for Neural Machine Translation},
author={Xiangpeng Wei and Heng Yu and Yue Hu and Yue Zhang and Rongxiang Weng and Weihua Luo},
booktitle={Annual Meeting of the Association for Computational Linguistics},
publisher={Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
......@@ -5824,7 +5725,7 @@ author = {Yoshua Bengio and
Lukasz Kaiser and
Anselm Levskaya},
title = {Reformer: The Efficient Transformer},
publisher = {OpenReview.net},
journal = {International Conference on Learning Representations},
year = {2020}
}
......@@ -5839,7 +5740,7 @@ author = {Yoshua Bengio and
@article{li2020shallow,
title={Shallow-to-Deep Training for Neural Machine Translation},
author={Li, Bei and Wang, Ziyang and Liu, Hui and Jiang, Yufan and Du, Quan and Xiao, Tong and Wang, Huizhen and Zhu, Jingbo},
publisher={Conference on Empirical Methods in Natural Language Processing},
journal={Conference on Empirical Methods in Natural Language Processing},
year={2020}
}
%%%%% chapter 12------------------------------------------------------
......@@ -6655,6 +6556,627 @@ author = {Yoshua Bengio and
pages = {60},
year = {2019}
}
@inproceedings{DBLP:conf/naacl/MohiuddinJ19,
author = {Tasnim Mohiuddin and
Shafiq R. Joty},
title = {Revisiting Adversarial Autoencoder for Unsupervised Word Translation
with Cycle Consistency and Improved Training},
pages = {3857--3867},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2019}
}
@inproceedings{DBLP:conf/acl/HuangQC19,
author = {Jiaji Huang and
Qiang Qiu and
Kenneth Church},
title = {Hubless Nearest Neighbor Search for Bilingual Lexicon Induction},
pages = {4072--4080},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2019}
}
@article{DBLP:journals/corr/abs-1811-01124,
author = {Jean Alaux and
Edouard Grave and
Marco Cuturi and
Armand Joulin},
title = {Unsupervised Hyperalignment for Multilingual Word Embeddings},
journal = {CoRR},
volume = {abs/1811.01124},
year = {2018}
}
@inproceedings{DBLP:conf/emnlp/XuYOW18,
author = {Ruochen Xu and
Yiming Yang and
Naoki Otani and
Yuexin Wu},
title = {Unsupervised Cross-lingual Transfer of Word Embedding Spaces},
pages = {2465--2474},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
@inproceedings{DBLP:conf/emnlp/DouZH18,
author = {Zi-Yi Dou and
Zhi-Hao Zhou and
Shujian Huang},
title = {Unsupervised Bilingual Lexicon Induction via Latent Variable Models},
pages = {621--626},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
@inproceedings{DBLP:conf/emnlp/HoshenW18,
author = {Yedid Hoshen and
Lior Wolf},
title = {Non-Adversarial Unsupervised Word Translation},
pages = {469--478},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
@inproceedings{DBLP:conf/emnlp/KimGN18,
author = {Yunsu Kim and
Jiahui Geng and
Hermann Ney},
title = {Improving Unsupervised Word-by-Word Translation with Language Model
and Denoising Autoencoder},
pages = {862--868},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
@inproceedings{DBLP:conf/emnlp/MukherjeeYH18,
author = {Tanmoy Mukherjee and
Makoto Yamada and
Timothy M. Hospedales},
title = {Learning Unsupervised Word Translations Without Adversaries},
pages = {627--632},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
@inproceedings{DBLP:conf/emnlp/JoulinBMJG18,
author = {Armand Joulin and
Piotr Bojanowski and
Tomas Mikolov and
Herv{\'{e}} J{\'{e}}gou and
Edouard Grave},
title = {Loss in Translation: Learning Bilingual Word Mapping with a Retrieval
Criterion},
pages = {2979--2984},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
@inproceedings{DBLP:conf/emnlp/ChenC18,
author = {Xilun Chen and
Claire Cardie},
title = {Unsupervised Multilingual Word Embeddings},
pages = {261--270},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2018}
}
@inproceedings{DBLP:conf/naacl/MohiuddinJ19,
author = {Tasnim Mohiuddin and
Shafiq R. Joty},
title = {Revisiting Adversarial Autoencoder for Unsupervised Word Translation
with Cycle Consistency and Improved Training},
pages = {3857--3867},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2019}
}
@inproceedings{DBLP:conf/emnlp/TaitelbaumCG19,
author = {Hagai Taitelbaum and
Gal Chechik and
Jacob Goldberger},
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publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2019}
}
@inproceedings{DBLP:conf/acl/YangLCLS19,
author = {Pengcheng Yang and
Fuli Luo and
Peng Chen and
Tianyu Liu and
Xu Sun},
title = {{MAAM:} {A} Morphology-Aware Alignment Model for Unsupervised Bilingual
Lexicon Induction},
pages = {3190--3196},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2019}
}
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author = {Aitor Ormazabal and
Mikel Artetxe and
Gorka Labaka and
Aitor Soroa and
Eneko Agirre},
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year = {2019}
}
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author = {Mikel Artetxe and
Gorka Labaka and
Eneko Agirre},
title = {Bilingual Lexicon Induction through Unsupervised Machine Translation},
pages = {5002--5007},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2019}
}
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Anna Korhonen and
Goran Glavas},
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year={2018}
}
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Mareike Hartmann and
Anders S{\o}gaard},
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Induction},
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year = {2019}
}
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year = {2019}
}
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year = {2019}
}
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author = {Armand Joulin and
Piotr Bojanowski and
Tomas Mikolov and
Herv{\'{e}} J{\'{e}}gou and
Edouard Grave},
title = {Loss in Translation: Learning Bilingual Word Mapping with a Retrieval
Criterion},
pages = {2979--2984},
publisher = {Annual Meeting of the Annual Meeting of the Association for Computational Linguistics},
year = {2018}
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Sebastian Ruder and
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year = {2018}
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%%%%% chapter 16------------------------------------------------------
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
......
mt-book-xelatex.tex
......@@ -139,14 +139,14 @@
%\include{Chapter6/chapter6}
%\include{Chapter7/chapter7}
%\include{Chapter8/chapter8}
\include{Chapter9/chapter9}
\include{Chapter10/chapter10}
\include{Chapter11/chapter11}
\include{Chapter12/chapter12}
%\include{Chapter9/chapter9}
%\include{Chapter10/chapter10}
%\include{Chapter11/chapter11}
%\include{Chapter12/chapter12}
%\include{Chapter13/chapter13}
%\include{Chapter14/chapter14}
%\include{Chapter15/chapter15}
%\include{Chapter16/chapter16}
\include{Chapter16/chapter16}
%\include{Chapter17/chapter17}
%\include{Chapter18/chapter18}
%\include{ChapterAppend/chapterappend}
......
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