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\title[Models of SCFG Induction]{Models of Synchronous Grammar Induction for SMT}

\author[CLSP Workshop 2010]{
  Workshop 2010
  %Phil Blunsom$^1$ \and Trevor Cohn$^2$ \and Chris Dyer$^3$ \and Adam Lopez$^4$
}

\institute[Baltimore]{
  The Center for Speech and Language Processing \\ Johns Hopkins University
%  $^1$University of Oxford\\ 
%  $^2$University of Sheffield\\ 
%  $^3$Carnegie Mellon University\\ 
%  $^4$University of Edinburgh
}
\date[June 21]{June 21, 2010}

%\subject{Unsupervised models of Synchronous Grammar Induction for SMT}

%\pgfdeclareimage[height=1.0cm]{university-logo}{logo}
%\logo{\pgfuseimage{university-logo}}

%\AtBeginSection[]
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\begin{document}

\begin{frame}
  \titlepage
\end{frame}

%\begin{frame}{Outline}
%  \tableofcontents
% You might wish to add the option [pausesections]
%\end{frame}
 
%\begin{frame}{Outline}
%  \tableofcontents
%  % You might wish to add the option [pausesections]
%\end{frame}


\begin{frame}[t]{Team members}
\begin{center}
{\bf Senior Members} \\
  Phil Blunsom (Oxford)\\
  Trevor Cohn (Sheffield)\\
  Adam Lopez (Edinburgh/COE)\\
  Chris Dyer (CMU)\\
  Jonathan Graehl (ISI)\\
\vspace{0.2in}
{\bf Graduate Students} \\
  Jan Botha (Oxford) \\
  Vladimir Eidelman (Maryland) \\
  Ziyuan Wang (JHU) \\
  ThuyLinh Nguyen (CMU) \\
\vspace{0.2in}
{\bf Undergraduate Students} \\
  Olivia Buzek (Maryland) \\
  Desai Chen (CMU) \\
\end{center}
\end{frame}



\begin{frame}[t]{Statistical machine translation}
%\vspace{1.0cm}
\begin{exampleblock}{Urdu $\rightarrow$ English}
  \begin{figure}
    {\centering \includegraphics[scale=0.55]{urdu.pdf}}
  \end{figure}
\vspace{0.10cm}
\end{exampleblock}
\begin{itemize}
  \item Statistical machine translation: Learn how to translate from parallel corpora.
\end{itemize}
\end{frame}


\begin{frame}[t]{Statistical machine translation: }
%\vspace{1.0cm}
\begin{exampleblock}{Urdu $\rightarrow$ English}
  \begin{figure}
    {\centering \includegraphics[scale=0.55]{urdu-ref.pdf}}
  \end{figure}
\end{exampleblock}
\begin{itemize}
  \item Statistical machine translation: Learn how to translate from parallel corpora 
\end{itemize}
\end{frame}

\begin{frame}[t]{Statistical machine translation: Before}
%\vspace{1.0cm}
\begin{exampleblock}{Urdu $\rightarrow$ English}
  \begin{figure}
    {\centering \includegraphics[scale=0.55]{urdu-bl.pdf}}
  \end{figure}
\end{exampleblock}
\begin{itemize}
  \item Current state-of-the-art translation models struggle with language pairs which exhibit large differences in structure.
\end{itemize}
\end{frame}

\begin{frame}[t]{Statistical machine translation: After}
%\vspace{1.0cm}
\begin{exampleblock}{Urdu $\rightarrow$ English}
  \begin{figure}
    {\centering \includegraphics[scale=0.55]{urdu-25hp.pdf}}
  \end{figure}
\end{exampleblock}
\begin{itemize}
  \item In this workshop we've made some small steps towards better translations for difficult language pairs.
\end{itemize}
\end{frame}


\begin{frame}[t]{Statistical machine translation: limitations}
\vspace{1.0cm}
\begin{exampleblock}{Structural divergence between languages:}
  %\vspace{0.3cm}
  \begin{table}
  \centering
    \only<1>{
    \begin{tabular}{|l|l|}
    \hline
      {\bf English}  & {\bf Who wrote this letter?} \\
      \hline
      Arabic   & \begin{Geeza}من الذي كتب هذه الرسالة؟\end{Geeza} \\
               & \textcolor{gray}{(function-word)} (who) (wrote) (this) (the-letter) \\
      \hline
      Chinese  & \begin{CJK}这封  信  是  谁  写  的 ？\end{CJK} \\
               & (this) (letter) (be) (who) (write) (come-from) \textcolor{gray}{(function-word)} \\
      \hline
    \end{tabular}
    }
    \only<2>{
    \begin{tabular}{|l|l|}
    \hline
      {\bf English}  & {\bf \textcolor{blue}{Who} \textcolor{green}{wrote} \textcolor{red}{this} \textcolor{orange}{letter?}} \\
      \hline
      Arabic   & \begin{Geeza}من الذي كتب هذه الرسالة؟\end{Geeza} \\
               & \textcolor{gray}{(function-word)} \textcolor{blue}{(who)} \textcolor{green}{(wrote)} \textcolor{red}{(this)} \textcolor{orange}{(the-letter)} \\
      \hline
      Chinese  & \begin{CJK}这封  信  是  谁  写  的 ？\end{CJK} \\
               & (this) (letter) (be) (who) (write) (come-from) \textcolor{gray}{(function-word)} \\
      \hline
    \end{tabular}
    }
    \only<3->{
    \begin{tabular}{|l|l|}
    \hline
      {\bf English}  & {\bf \textcolor{blue}{Who wrote} \textcolor{red}{this letter}?} \\
      \hline
      Arabic   & \begin{Geeza}من الذي كتب هذه الرسالة؟\end{Geeza} \\
               & \textcolor{gray}{(function-word)} (who) (wrote) (this) (the-letter) \\
      \hline
      Chinese  & \begin{CJK}\textcolor{red}{这封  信}  \textcolor{blue}{是  谁  写}  的 ？\end{CJK} \\
               & \textcolor{red}{(this) (letter)} \textcolor{blue}{(be) (who) (write) (come-from)} \textcolor{gray}{(function-word)} \\
      \hline
    \end{tabular}
  }
  \end{table}
\end{exampleblock}
\only<4>{
  \begin{itemize}
  \item Phrasal translation equivalences \textcolor{green}{(existing models)}
  \item {\bf Constituent reordering \textcolor{blue}{(this workshop!)}}
  \item Morphology \textcolor{red}{(Next year?)}
  \end{itemize}
}
\end{frame}

\begin{frame}[t]{Statistical machine translation: successes}
\begin{center}
  \includegraphics[scale=0.35]{GoogleTranslateLanguages.pdf}
\end{center}
\end{frame}

\begin{frame}[t]{Workshop overview}
Input:
  \begin{itemize}
%  \item Joshua decoder
  \item Existing procedures for synchronous grammar extraction
  \end{itemize}
\vspace{0.3in}
Output:
  \begin{itemize}
    \item New unsupervised models for large scale synchronous grammar extraction,
%    \item An implementation of this model,
    \item A comparison and analysis of the existing and proposed models,
    \item Extended decoders (cdec/Joshua) capable of working efficiently with these models.
  \end{itemize}
\end{frame}

\begin{frame}[t]{Models of translation}
\begin{exampleblock}{Supervised SCFG: Syntactic Tree-to-String}
\begin{center}
  \includegraphics[scale=0.55]{JeNeVeuxPasTravailler-tsg.pdf}
  \hspace{0.3in}
  \includegraphics[scale=0.55]{JeVeuxTravailler-tsg.pdf}
\end{center}
\end{exampleblock}
\begin{itemize}
\item Strong model of sentence structure.
\item Reliant on a treebank to train the parser.
\end{itemize}
\end{frame}

\begin{frame}[t]{Models of translation}
\begin{block}{Unlabelled SCFG: Hiero}
  \begin{center}
    \includegraphics[scale=0.55]{JeNeVeuxPasTravailler-Hiero.pdf}
    \hspace{0.3in}
    \includegraphics[scale=0.55]{JeVeuxTravailler-Hiero.pdf}
  \end{center}
\end{block}
\begin{itemize}
\item Only requires the parallel corpus.
\item But weak model of sentence structure.
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{Using syntax in Machine Translation:}
  \footnotesize
  \begin{block}{Synchronous Context Free Grammar (SCFG)}
    \begin{figure}
    \begin{align*}
        \alert<2>{S} & \alert<2>{\rightarrow  \langle X\ind{1},\ X\ind{1} \rangle} 
            &\quad \alert<3,5>{X} & \alert<3,5>{\rightarrow  \langle X\ind{1}\ X\ind{2},\ X\ind{1}\ X\ind{2} \rangle} \\
        \alert<7>{X} & \alert<7>{\rightarrow  \langle X\ind{1}\ X\ind{2},\ X\ind{2}\ X\ind{1} \rangle} & &\\
        \alert<4>{X} & \alert<4>{\rightarrow  \langle Sie,\ She \rangle} 
           &\quad \alert<6>{X} & \alert<6>{\rightarrow  \langle will,\ wants\ to \rangle} \\
         \alert<8>{X} & \alert<8>{\rightarrow  \langle eine\ Tasse\ Kaffee,\ a\ cup\ of\ coffee \rangle}
           &\quad \alert<9>{X} & \alert<9>{\rightarrow  \langle trinken,\ drink\rangle} \\
    \end{align*}
    \end{figure}
  \end{block}
  \begin{exampleblock}{Example Derivation}
      %\begin{figure}
      %\begin{align*}
      \center
        \vspace{0.2cm}
        \onslide<2->{\alert<2>{$S \Rightarrow \langle X\ind{1},\ X\ind{1}\ \rangle$}} \quad \onslide<3->{\alert<3>{$\Rightarrow \langle X\ind{2}\ X\ind{3},\ X\ind{2}\ X\ind{3} \rangle$ \\}}
        \vspace{0.2cm}
        \onslide<4->{\alert<4>{$\Rightarrow \langle Sie\ X\ind{3},\ She\ X\ind{3} \rangle$}} \quad \onslide<5->{\alert<5>{$\Rightarrow \langle Sie\ X\ind{4}\ X\ind{5},\ She\ X\ind{4}\ X\ind{5} \rangle$ \\}}
        \vspace{0.2cm}
        \onslide<6->{\alert<6>{$\Rightarrow \langle Sie\ will\ X\ind{5},\ She\ wants\ to\  X\ind{5} \rangle$ }} \quad \onslide<7->{\alert<7>{$\Rightarrow \langle Sie\ will\ X\ind{6} X\ind{7},\ She\ wants\ to\  X\ind{7} X\ind{6} \rangle$ \\}}
        \vspace{0.2cm}
        \onslide<8->{\alert<8>{$\Rightarrow \langle Sie\ will\ eine\ Tasse\ Kaffee\ X\ind{7},\ She\ wants\ to\ X\ind{7}\ a\ cup\ of\ coffee\rangle$} \\}
        \vspace{0.2cm}
        \onslide<9->{\alert<9>{$\Rightarrow \langle Sie\ will\ eine\ Tasse\ Kaffee\ trinken,\ She\ wants\ to\ drink\ a\ cup\ of\ coffee\rangle$}}
        \vspace{0.2cm}
      %\end{align*}
      %\end{figure}
  \end{exampleblock}
\end{frame}

\begin{frame}[t]{Models of translation}
\begin{exampleblock}{Phrase extraction:}
\begin{center}
\only<1>{\includegraphics[scale=0.8]{PhraseExtraction6.pdf}\\[1cm]}
\only<2>{\includegraphics[scale=0.8]{PhraseExtraction5.pdf}\\[1cm]}
\only<3>{\includegraphics[scale=0.8]{PhraseExtraction4.pdf}\\[1cm]}
\only<4>{\includegraphics[scale=0.8]{PhraseExtraction3.pdf}\\[1cm]}
\only<5>{\includegraphics[scale=0.8]{PhraseExtraction2.pdf}\\[1cm]}
\only<6>{\includegraphics[scale=0.8]{PhraseExtraction1.pdf}\\[1cm]}
\only<7>{\includegraphics[scale=0.8]{PhraseExtraction.pdf}\\[1cm]}
\end{center}
\end{exampleblock}
\only<2->{
\begin{unpacked_itemize}
\only<2>{\item Use a word-based translation model to annotate the parallel corpus with word-alignments}
\only<3->{\item $\langle$ Je, I $\rangle$, $\langle$ veux, want to $\rangle$,  $\langle$ travailler, work $\rangle$}\only<4->{, $\langle$ ne veux pas, do not want to $\rangle$}\only<5->{, $\langle$ ne veux pas travailler, do not want to work $\rangle$}\only<6->{, $\langle$ Je ne veux pas, I do not want to $\rangle$}\only<7->{, $\langle$ Je ne veux pas travailler, I do not want to work $\rangle$}
\end{unpacked_itemize}
}
\end{frame}


\begin{frame}[t]{Models of translation}
\begin{exampleblock}{SCFG Rule extraction:}
\begin{center}
\only<1>{\includegraphics[scale=0.8]{HieroExtraction1.pdf}\\[1cm]}
\only<2>{\includegraphics[scale=0.8]{HieroExtraction2.pdf}\\[1cm]}
\only<3>{\includegraphics[scale=0.8]{HieroExtraction3.pdf}\\[1cm]}
\only<4>{\includegraphics[scale=0.8]{HieroExtraction4.pdf}\\[1cm]}
\end{center}
\end{exampleblock}
\begin{unpacked_itemize}
\only<1>{ \item X -> $\langle$ ne veux pas, do not want to $\rangle$ }
\only<2>{ \item X -> $\langle$ ne veux pas, do not want to $\rangle$, \item X -> $\langle$ ne X\ind{1} pas, do not X\ind{1} $\rangle$ }
\only<3>{ \item VP$/$NN -> $\langle$ ne veux pas, do not want to $\rangle$, \item VP$/$NN -> $\langle$ ne V\ind{1} pas, do not V\ind{1} $\rangle$ }
\only<4>{ \item X10 -> $\langle$ ne veux pas, do not want to $\rangle$, \item X10 -> $\langle$ ne X14\ind{1} pas, do not X14\ind{1} $\rangle$ }
\end{unpacked_itemize}
\end{frame}

%\begin{frame}[t]{Models of translation}
%\begin{block}{Hierarchical}
%  \begin{center}
%    \includegraphics[scale=0.55]{JeNeVeuxPasTravailler-Hiero.pdf}
%    \hspace{0.3in}
%    \includegraphics[scale=0.55]{JeVeuxTravailler-Hiero.pdf}
%  \end{center}
%\end{block}
%\end{frame}


%\begin{frame}[t]{Impact}
%  \begin{center}
%    \includegraphics[scale=0.3]{ccb_tree.pdf}
%  \end{center}
%\end{frame}


\begin{frame}[t]{Impact}
\vspace{0.5in}
\begin{table}
  \begin{tabular}{l|rr}
    \hline
    Language & Words &  Domain \\ \hline
    English & 4.5M& Financial news \\
    Chinese & 0.5M & Broadcasting news \\ 
    Arabic &  300K (1M planned)  &  News  \\
    Korean & 54K  & Military \\ \hline
  \end{tabular}
\caption{Major treebanks: data size and domain \label{table_treebanks_size}}
\end{table}
\end{frame}


\begin{frame}[t]{Impact}
Parallel corpora far exceed treebanks (millions of words):
  \begin{figure}
    {\centering \includegraphics[scale=0.7]{resource_matrix.pdf}}
  \end{figure}
\end{frame}


\begin{frame}[t]{Models of translation}
\begin{block}{Hierarchical}
  \begin{center}
    \includegraphics[scale=0.55]{JeNeVeuxPasTravailler-Hiero-labelled.pdf}
    \hspace{0.3in}
    \includegraphics[scale=0.55]{JeVeuxTravailler-Hiero-labelled.pdf}
  \end{center}
\end{block}
\begin{itemize}
\item \alert{AIM: Implement a large scale open-source synchronous constituent learning system.} 
\item \alert{AIM: Investigate and understand the relationship between the choice of synchronous grammar and SMT performance,} 
\item \alert{AIM: and fix our decoders accordingly.} 
\end{itemize}
\end{frame}

\begin{frame}[t]{Evaluation goals}
We will predominately evaluate using BLEU, but also use automatic structured metrics and perform small scale human evaluation:
\vspace{0.25in}
\begin{unpacked_itemize}
\item Evaluate phrasal, syntactic, unsupervised syntactic,
\item Aim 1: Do no harm (not true of existing syntactic approach)
\item Aim 2: Exceed the performance of current non-syntactic systems.
\item Aim 3: Meet or exceed performance of existing syntactic systems.
\end{unpacked_itemize}
\end{frame}

%\begin{frame}[t]{Impact}
%Success will have a significant impact on two areas of CL:
%\vspace{0.25in}
%\begin{unpacked_itemize}
%\item Machine translation
%\begin{unpacked_itemize}
%  \item Make the benefits of richly structured translation models available to a much wider range of researchers and for a wider range of languages.
%% \item Change the research outlook of the field.
%\end{unpacked_itemize}
%\item Grammar induction:
%\begin{unpacked_itemize}
%  \item Provide an empirical validation of state-of-the-art grammar induction techniques.
%\end{unpacked_itemize}
%\end{unpacked_itemize}
%\end{frame}


\begin{frame}[t]{Workshop Streams}
\vspace{0.25in}
\begin{unpacked_itemize}
\item Implement scalable SCFG grammar extraction algorithms.
\item Improve SCFG decoders to effieciently handle the grammars produce.
\item Investigate discriminative training regimes the leverage features extracted from these grammars.
\end{unpacked_itemize}
\end{frame}



\begin{frame}[t]{Language pairs (small)}
\begin{itemize}
\item BTEC Chinese-English:
  \begin{itemize}
  \item 44k sentence pairs, short sentences
  \item Widely reported `prototyping' corpus
  \item Hiero baseline score: 52.4 (16 references)
  \item Prospects: BTEC always gives you good results
  \end{itemize}
\item NIST Urdu-English:
  \begin{itemize}
  \item 50k sentence pairs
  \item Hiero baseline score: MT05 - 23.7 (4 references)
  \item Major challenges: major long-range reordering, SOV word order
  \item Prospects: small data, previous gains with supervised syntax
  \end{itemize}
\end{itemize}
\end{frame}

\begin{frame}[t]{Language pairs (large)}
\begin{itemize}
\item NIST Chinese-English:
  \begin{itemize}
  \item 1.7M sentence pairs, Standard NIST test sets
  \item Hiero baseline score: MT05 - 33.9 (4 references)
  \item Major challenges: large data, mid-range reordering, lexical ambiguity
  \item Prospects: supervised syntax gains reported
  \end{itemize}
\item NIST Arabic-English:
  \begin{itemize}
  \item 900k sentence pairs
  \item Hiero baseline score: MT05 - 48.9 (4 references)
  \item Major challenges: strong baseline, local reordering, VSO word order
  \item Prospects: difficult
  \end{itemize}
\item Europarl Dutch-French:
  \begin{itemize}
  \item 1.5M sentence pairs, standard Europarl test sets
  \item Hiero baseline score: Europarl 2008 - 26.3 (1 reference)
  \item Major challenges: V2 / V-final word order, many non-literal translations
  \item Prospects: ???
  \end{itemize}
\end{itemize}
\end{frame}


\begin{frame}[t]{Summary}
\begin{itemize}
\item Scientific Merit:
  \begin{itemize}
  \item A systematic comparison of existing syntactive approaches to SMT.
  \item An empirical study of how constituency is useful in SMT.
  \item An evaluation of existing theories of grammar induction in a practical application (end-to-end evaluation).
  \end{itemize}
\item Potential Impact:
  \begin{itemize}
  \item Better MT systems, for more languages, across a range of domains.
  \item More accessible high performance translation models for researchers. % all over the world.
  \end{itemize}
\item Feasibility:
  \begin{itemize}
  \item A great team with a wide range of both theoretical and practical experience.
  %\item Incremental plan without any deal breaking dependencies.
  \item Solid preparation.
  \end{itemize}
\item Novelty:
  \begin{itemize}
  \item First attempt at large scale unsupervised synchronous grammar induction.
%  \item First study seeking to compare and understand the impact of synchronous structure on translation performance. 
  \end{itemize}
\end{itemize}
\end{frame}


\end{document}
