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Toy-MT-Introduction
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Merge branch 'master' of 47.105.50.196:NiuTrans/Toy-MT-Introduction

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Section06-Neural-Machine-Translation/section06.tex
......@@ -3243,12 +3243,6 @@ $\textrm{``you''} = \argmax_{y} \textrm{P}(y|\textbf{s}_1, \alert{\textbf{C}})$
\end{frame}
%%%------------------------------------------------------------------------------------------------------------
%%% 实验结果
\begin{frame}{效果}
%% 实用注意力机制带来的提升
%% 个大评测比赛没有不使用注意力机制的系统，已经成为标配
\end{frame}
%%%------------------------------------------------------------------------------------------------------------
%%% GNMT
......@@ -3494,6 +3488,46 @@ $\textrm{``you''} = \argmax_{y} \textrm{P}(y|\textbf{s}_1, \alert{\textbf{C}})$
\end{frame}
%%%------------------------------------------------------------------------------------------------------------
%%% 实验结果
\begin{frame}{效果}
%% 实用注意力机制带来的提升
%% 个大评测比赛没有不使用注意力机制的系统，已经成为标配
\begin{itemize}
\item 在引入注意力机制之前，神经机器翻译（RNNSearch）的性能要弱于统计机器翻译（PBMT）
\item 加入注意力机制和深层网络之后，神经机器翻译性能有了很大幅度的提升
\item 虽然网络深度增加了，但是通过相应的结构设计和解码策略保证了解码速度
\end{itemize}
{
\footnotesize
\begin{center}
\setlength{\tabcolsep}{3pt}
\renewcommand\arraystretch{1}
\begin{tabular}{l}
\begin{tabular}{lcccl}
\specialrule{1pt}{1pt}{1pt}
\multirow{2}{*}{\#} & \multicolumn{2}{c}{BLEU} & \multicolumn{2}{c}{ \multirow{2}{*}{CPU decoding time}}\\
\cline{2-3}
& EN-DE & EN-FR & \\
\specialrule{0.6pt}{1pt}{1pt}
PBMT & 20.7 & 37.0 & \multicolumn{2}{c}{-} \\
RNNSearch & 16.5 & - & \multicolumn{2}{c}{-} \\
LSTM(6 layers) & - & 31.5 & \multicolumn{2}{c}{-} \\
Deep-Att & 20.6 & 37.7 & \multicolumn{2}{c}{-} \\
\specialrule{0.6pt}{1pt}{1pt}
GNMT & 24.6 & 39.0 & \multicolumn{2}{c}{0.2s per sentence} \\
\specialrule{1pt}{1pt}{1pt}
\end{tabular}\\
\addlinespace[-0.3ex]
\footnote *GNMT versus previous state-of-the-art models\\
\end{tabular}
\end{center}
}
\end{frame}
%%%------------------------------------------------------------------------------------------------------------
\section{Transformer}
%%%------------------------------------------------------------------------------------------------------------
......@@ -4863,19 +4897,14 @@ x_{l+1} = x_l+\mathcal{F}(x_l)
\begin{frame}{优化器}
% Adam
% 学习率调整
\end{frame}
%%%------------------------------------------------------------------------------------------------------------
\begin{frame}{训练配置}
\begin{itemize}
\item \textbf{优化器}：使用Adam优化器，$\beta_1$=0.9，$\beta_2$=0.98，$\epsilon=10^{-9}$ 关于学习率的设置，引入了warmup策略，在训练初期，学习率从一个较小的初始值逐渐增大，当到达一定的步数，学习率再逐渐减小
\item \textbf{优化器}：使用Adam优化器，$\beta_1$=0.9，$\beta_2$=0.98，$\epsilon=10^{-9}$
\item \textbf{学习率}：关于学习率的设置，引入了warmup策略，在训练初期，学习率从一个较小的初始值逐渐增大，当到达一定的步数，学习率再逐渐减小
\begin{displaymath}
lrate=d_{\mathrm{model}}^{-0.5}\cdot \min(step^{-0.5},step\cdot \mathrm{warmup\_steps}^{-1.5})
\end{displaymath}
这样做可以减缓在训练初期的不稳定现象，保持分布平稳，通常warmup\_steps通常设置为4000
\vspace{0.5em}
\only<1>{
......@@ -4902,7 +4931,45 @@ x_{l+1} = x_l+\mathcal{F}(x_l)
\caption{}\label{}
\end{figure}
}
\end{itemize}
\end{frame}
%%%------------------------------------------------------------------------------------------------------------
\begin{frame}{训练配置}
\begin{itemize}
\item \textbf{Mini Batch训练}：选择批量的数据作为训练样本，来计算损失函数，提高训练效率
\begin{itemize}
\item Mini Batch大小通常设置为2048/4096(token数)
\item 通常对句子长度进行排序，选取长度相近的句子组成一个batch，可以减少padding数量，提高训练效率
\end{itemize}
\vspace{0.5em}
\begin{center}
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\node [anchor=east] (label2) at ([xshift=-0.8em,yshift=0.6em]s4.west) {\scriptsize{Sorted:}};
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\end{center}
\item<2-> \textbf{Dropout }：为了防止网络训练过拟合，加入了Dropout操作。在四个地方用到了Dropout，词嵌入和位置编码、残差连接、注意力操作和前馈神经网络。Drop率通常设置为0.1
......@@ -4964,173 +5031,180 @@ x_{l+1} = x_l+\mathcal{F}(x_l)
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\end{scope}
\end{tikzpicture}
\end{center}
\end{scope}
\end{tikzpicture}
\end{center}
}
\end{frame}
......@@ -5458,7 +5532,32 @@ x_{l+1} = x_l+\mathcal{F}(x_l)
%%%------------------------------------------------------------------------------------------------------------
%%% open source NMT
\begin{frame}{一些开源NMT系统}
\end{frame}
\begin{itemize}
\item Tensor2Tensor
\begin{itemize}
\item Google Brain开发，基于静态图实现
\item 先定义、后运行、速度快、可优化，但是代码中的错误难以发现
\item https://github.com/tensorflow/tensor2tensor
\end{itemize}
\vspace{0.2em}
\item Fairseq
\begin{itemize}
\item Facebook开发，基于动态图实现
\item 灵活，debug方便，更适合自然语言处理
\item https://github.com/pytorch/fairseq
\end{itemize}
\vspace{0.2em}
\item NiuTrans.NMT
\begin{itemize}
\item 小牛翻译开发，基于动态图实现
\item 简单小巧，易于修改、C语言编写，代码高度优化
\item https://github.com/NiuTrans/NiuTensor
\end{itemize}
\vspace{0.2em}
\item 其他优秀的开源NMT系统：OpenNMT、THUMT
\end{itemize}
\end{frame}
%%%------------------------------------------------------------------------------------------------------------
%%% last slide
......
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