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\setbeamertemplate{navigation symbols}{\begin{CJK}{UTF8}{\mycfont} 第六章 神经机器翻译 \hspace*{2em} 肖桐\&朱靖波 \end{CJK} \hspace*{2em} \today \hspace*{2em} \insertframenumber{}/\inserttotalframenumber}

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\title{\Large{神经机器翻译}}
\author{\large{\textbf{肖桐\ \ 朱靖波}}}
\institute{
\blue{\url{xiaotong@mail.neu.edu.cn}} \black{} \\
\blue{\url{zhujingbo@mail.neu.edu.cn}} \black{} \\
\vspace{1.0em}
东北大学 自然语言处理实验室 \\
\blue{\underline{\url{http://www.nlplab.com}}} \black{} \\
\vspace{0.2cm}
\hspace{0.1cm} \includegraphics[scale=0.1]{../Figures/logo.pdf}
}
\date{}

\maketitle

\setlength{\leftmargini}{1em}
\setlength{\leftmarginii}{1em}

%%%------------------------------------------------------------------------------------------------------------
\section{编码器-解码器框架}

%%%------------------------------------------------------------------------------------------------------------
\section{循环神经网络翻译模型及注意力机制}

%%%------------------------------------------------------------------------------------------------------------
\subsection{起源}

%%%------------------------------------------------------------------------------------------------------------
%%% 神经机器翻译的历史
\begin{frame}{最初的神经机器翻译}

\begin{itemize}
\item 神经网络的在机器翻译中并不新鲜，在很多模块中早有实现，比如，翻译候选打分、语言模型等
    \begin{itemize}
    \item 但是，整个框架仍然是统计机器翻译
    \end{itemize}
\item<2-> 基于神经元网络的端到端建模出现在2013-2015，被称为\alert{Neural Machine Translation (NMT)}，一些代表性工作：
\end{itemize}

\visible<2->{
\begin{center}
{\footnotesize
\begin{tabular}{l | l | l}
\textbf{时间} & \textbf{作者} & \textbf{论文} \\ \hline
2013 & Kalchbrenner和 & Recurrent Continuous Translation Models \\
     & Blunsom        & \\
2014 & Sutskever等 & Sequence to Sequence Learning with \\
     &             & neural networks \\
2014 & Cho等 & Learning Phrase Representations using \\
     &       & RNN Encoder-Decoder for Statistical \\
     &       & Machine Translation \\
2014 & Cho等 & On the Properties of Neural Machine \\
     &       & Translation \\
2015 & Jean等 & On Using Very Large Target Vocabulary \\
     &        & for Neural Machine Translation 
\end{tabular}
}
\end{center}
}

\end{frame}

%%%------------------------------------------------------------------------------------------------------------
%%% 逐渐崛起的NMT
\begin{frame}{崛起}
\begin{itemize}
\item 2015年前统计机器翻译(SMT)在NLP是具有统治力的
    \begin{itemize}
    \item 当时NMT的系统还很初步，被SMT碾压
    \item 大多数的认知还没有进化到NMT年代，甚至Kalchbrenner等人早期的报告也被人质疑
    \end{itemize}
\item 2016年情况大有改变，当时非常受关注的一项工作是Google上线了神经机器翻译系统GNMT
    \begin{itemize}
    \item 在GNMT前后，百度、微软、小牛翻译等也分别推出了自己的神经机器翻译系统，出现了百花齐放的局面
    \end{itemize}
\end{itemize}
\begin{center}
\includegraphics[scale=0.35]{./Figures/google-news.png}
\end{center}
\end{frame}

%%%------------------------------------------------------------------------------------------------------------
%%% 模型结构
\begin{frame}{基于循环神经网络的翻译模型}
\begin{itemize}
\item 一种简单的模型
\end{itemize}
%%% 图
\end{frame}

%%%------------------------------------------------------------------------------------------------------------
%%% LSTM
\begin{frame}{长短时记忆模型(LSTM) (2页？)}
\begin{itemize}
\item LSTM
\end{itemize}
%%% 图
\end{frame}

%%%------------------------------------------------------------------------------------------------------------
%%% LSTM
\begin{frame}{门循环单元(GRU)}
\begin{itemize}
\item GRU
\end{itemize}
%%% 图
\end{frame}

%%%------------------------------------------------------------------------------------------------------------
%%% 一些变种
\begin{frame}{进一步的改进}
\begin{itemize}
\item 多层网络
\item fine-tuning
\end{itemize}
%%% 图
\end{frame}

%%%------------------------------------------------------------------------------------------------------------
\subsection{模型结构}

%%%------------------------------------------------------------------------------------------------------------
\subsection{注意力机制}

%%%------------------------------------------------------------------------------------------------------------
\section{Transformer}

%%%------------------------------------------------------------------------------------------------------------
\subsection{多头自注意力模型}

%%%------------------------------------------------------------------------------------------------------------
\subsection{训练}

%%%------------------------------------------------------------------------------------------------------------
\subsection{推断}

%%%------------------------------------------------------------------------------------------------------------
\subsection{深层网络}

%%%------------------------------------------------------------------------------------------------------------
\section{其它应用}

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\end{document}
