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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 28]{June 28, 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{frame}<beamer>{Outline}
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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)\\
%  Chris Callison-Burch (JHU)\\
%\vspace{0.1in}
%{\bf Graduate Students} \\
%  Jan Botha (Oxford) \\
%  Vladimir Eidelman (Maryland) \\
%  Ziyuan Wang (JHU) \\
%  ThuyLinh Nguyen (CMU) \\
%\vspace{0.1in}
%{\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: state-of-the-art}
%\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: successes}
\begin{center}
  \includegraphics[scale=0.35]{GoogleTranslateLanguages.pdf}
\end{center}
\end{frame}

\begin{frame}[t]{Statistical machine translation: limitations}
\vspace{1.0cm}
  \begin{table}
  \centering
    \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)} \textcolor{blue}{(who)} \textcolor{green}{(wrote)} \textcolor{red}{(this)} \textcolor{orange}{(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{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
  \item Constituent reordering
  \item Morphology
  \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{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{unpacked_itemize}
\only<1>{\item S -> $\langle$ X\ind{1}, X\ind{1} $\rangle$, \\ S -> $\langle$ S\ind{1} X\ind{2}, S\ind{1} X\ind{2} $\rangle$}
\only<1>{\item X -> $\langle$ Je, I $\rangle$, \hspace{0.5in} X -> $\langle$ ne X\ind{1} pas, do not X\ind{1} $\rangle$, \\ X -> $\langle$ veux, want to$\rangle$, X -> $\langle$ travailler, work $\rangle$}
\only<2->{
\item Only requires the parallel corpus.
\item But weak model of sentence structure.
}
\end{unpacked_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}
\only<1>{\item S -> $\langle$ NP\ind{1} VP\ind{2}, NP\ind{1} VP\ind{2} $\rangle$, \\ NP -> $\langle$ PRP\ind{1}, PRP\ind{1} $\rangle$}
\only<1>{\item PRP -> $\langle$ Je, I $\rangle$, VB -> $\langle$ travailler, work $\rangle$ \\ VP -> $\langle$ ne veux pas VB\ind{1}, do not want to VB\ind{1} $\rangle$}
\only<2->{
  \item Strong model of sentence structure.
  \item Reliant on a treebank to train the parser.
}
\end{itemize}
\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{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}
\vspace{0.25in}
\begin{block}{This workshop}
  \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{unpacked_itemize}
\only<1>{\item S -> $\langle$ X4\ind{1} X2\ind{2}, X4\ind{1} X2\ind{2} $\rangle$, X4 -> $\langle$ X1\ind{1} X10\ind{2}, X1\ind{1} X10\ind{2} $\rangle$}
\only<1>{\item X1 -> $\langle$ Je, I $\rangle$, X10 -> $\langle$ ne X14\ind{1} pas, do not X14\ind{1} $\rangle$, \\ X14 -> $\langle$ veux, want to$\rangle$, X10 -> $\langle$ travailler, work $\rangle$}
\only<2->{
  \item Only requires the parallel corpus.
  \item But also gives a strong model of sentence structure.
}
\end{unpacked_itemize}
\end{frame}


\begin{frame}[t]{Workshop overview}
Input:
  \begin{itemize}
%  \item Joshua decoder
  \item Existing procedures for unlabelled 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{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]{Models of translation}
%\vspace{0.25in}
%\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}
\begin{enumerate}
\item Implement scalable labelled SCFG grammar induction algorithms:
\begin{itemize}
\item by clustering translation phrases which occur in the same context we can learn which phrases are substituteable,
\item we have implemented both parametric and non-parametric Bayesian clustering algorithms.
\end{itemize}
\item Improve SCFG decoders to efficiently handle the grammars produced:
\begin{itemize}
\item translation complexity scales quadratically as we add more categories,
\item in order to decode efficiently with the grammars we've induced we have created faster search algorithms tuned for syntactic grammars.
\end{itemize}
\item Investigate discriminative training regimes to leverage features extracted from these grammars:
\begin{itemize}
\item to make the most of our induced grammars we need discriminative training algorithms that learn from more than a handful of features,
\item we've implemented two large scale discriminative algorithms for training our models.
\end{itemize}
\end{enumerate}
\end{frame}

\begin{frame}[t]{Extrinsic evaluation: Bleu}
\begin{exampleblock}{
   \only<1>{Ngram overlap metrics:}
   \only<2>{Ngram overlap metrics: 1-gram precision $p_1 = \frac{11}{14}$}
   \only<3>{Ngram overlap metrics: 2-gram precision $p_2 = \frac{5}{13}$}
   \only<4>{Ngram overlap metrics: 3-gram precision $p_3 = \frac{2}{12}$}
   \only<5>{Ngram overlap metrics: 4-gram precision $p_4 = \frac{1}{11}$}
}
\vspace{0.2cm}
{\em Source}: \begin{CJK}欧盟 办事处 与 澳洲 大使馆 在 同 一 建筑 内 \end{CJK} \\
\vspace{0.3cm}
\only<1>{{\em Candidate}: the chinese embassy in australia and the eu representative office in the same building}
\only<2>{{\em Candidate}: \alert{the} chinese \alert{embassy} \alert{in} australia \alert{and} \alert{the} \alert{eu} representative \alert{office} \alert{in} \alert{the} \alert{same} \alert{building}}
\only<3>{{\em Candidate}: the chinese embassy in australia \alert{and} \alert{the} \alert{eu} representative office \alert{in} \alert{the} \alert{same} \alert{building}}
\only<4>{{\em Candidate}: the chinese embassy in australia and the eu representative office \alert{in} \alert{the} \alert{same} \alert{building}}
\only<5>{{\em Candidate}: the chinese embassy in australia and the eu representative office \alert{in} \alert{the} \alert{same} \alert{building}}
\vspace{0.2cm}
\end{exampleblock}

\begin{block}{Reference Translations:}
  \begin{enumerate}
  \only<1>{\item the eu office and the australian embassy are housed in the same building}
  \only<2>{\item \alert{the} \alert{eu} \alert{office} \alert{and} \alert{the} australian \alert{embassy} are housed \alert{in} \alert{the} \alert{same} \alert{building}}
  \only<3>{\item \alert{the} \alert{eu} office \alert{and} \alert{the} australian embassy are housed \alert{in} \alert{the} \alert{same} \alert{building}}
  \only<4>{\item the eu office and the australian embassy are housed \alert{in} \alert{the} \alert{same} \alert{building}}
  \only<5>{\item the eu office and the australian embassy are housed \alert{in} \alert{the} \alert{same} \alert{building}}

  \only<1>{\item the european union office is in the same building as the australian embassy}
  \only<2>{\item \alert{the} european union \alert{office} is \alert{in} \alert{the} \alert{same} \alert{building} as \alert{the} australian \alert{embassy}}
  \only<3>{\item the european union office is \alert{in} \alert{the} \alert{same} \alert{building} as the australian embassy}
  \only<4>{\item the european union office is \alert{in} \alert{the} \alert{same} \alert{building} as the australian embassy}
  \only<5>{\item the european union office is \alert{in} \alert{the} \alert{same} \alert{building} as the australian embassy}

  \only<1>{\item the european union 's office and the australian embassy are both located in the same building}
  \only<2>{\item \alert{the} european union 's \alert{office} \alert{and} \alert{the} australian \alert{embassy} are both located \alert{in} \alert{the} \alert{same} \alert{building}}
  \only<3>{\item the european union 's office \alert{and} \alert{the} australian embassy are both located \alert{in} \alert{the} \alert{same} \alert{building}}
  \only<4>{\item the european union 's office and the australian embassy are both located \alert{in} \alert{the} \alert{same} \alert{building}}
  \only<5>{\item the european union 's office and the australian embassy are both located \alert{in} \alert{the} \alert{same} \alert{building}}
  
  \only<1>{\item the eu 's mission is in the same building with the australian embassy}
  \only<2>{\item \alert{the} \alert{eu} 's mission is \alert{in} \alert{the} \alert{same} \alert{building} with \alert{the} australian \alert{embassy}}
  \only<3>{\item \alert{the} \alert{eu} 's mission is \alert{in} \alert{the} \alert{same} \alert{building} with the australian embassy}
  \only<4>{\item the eu 's mission is \alert{in} \alert{the} \alert{same} \alert{building} with the australian embassy}
  \only<5>{\item the eu 's mission is \alert{in} \alert{the} \alert{same} \alert{building} with the australian embassy}
  \end{enumerate}
\end{block}
\end{frame}

\begin{frame}[t]{Extrinsic evaluation: Bleu}
\begin{exampleblock}{BLEU}
\Large
\begin{align}
\nonumber BLEU_n = BP \times \exp{\left( \sum_{n=1}^{N} w_n \log{p_n} \right) }\\
\nonumber BP = \left\{ 
  \begin{array}{ll} 
    1 & \mbox{if $c > r$}  \\
    \exp{(1-\frac{R'}{C'})} & \mbox{if $c <= r$}
  \end{array} \right.
\end{align}
\end{exampleblock}
\begin{itemize}
\item {\em BP} is the {\em Brevity Penalty}, $w_n$ is the ngram length weights (usually $\frac{1}{n}$), $p_n$ is precision of ngram predictions, $R'$ is the total length of all references and $C'$ is the sum of the best matching candidates.
\item statistics are calculate over the whole {\em document}, i.e. all the sentences.
\end{itemize}
\end{frame}



\begin{frame}[t]{Language pairs}
\begin{unpacked_itemize}
\item BTEC Chinese-English:
  \begin{itemize}
  \item 44k sentence pairs, short sentences
  \item Widely reported `prototyping' corpus
  \item Hiero baseline score: 57.0 (16 references)
  \end{itemize}
\item NIST Urdu-English:
  \begin{itemize}
  \item 50k sentence pairs
  \item Hiero baseline score: 21.1 (4 references)
  \item Major challenges: major long-range reordering, SOV word order
  \end{itemize}
\item Europarl Dutch-French:
  \begin{itemize}
  \item 100k sentence pairs, standard Europarl test sets
  \item Hiero baseline score: Europarl 2008 - 15.75 (1 reference)
  \item Major challenges: V2 / V-final word order, morphology
  \end{itemize}
\end{unpacked_itemize}
\end{frame}


\begin{frame}[t]{Outline}
\begin{columns}
  \begin{column}{0.2\textwidth}
    \begin{exampleblock}{}
      \begin{figure}
        \tiny
        {\centering \includegraphics[scale=0.07]{trevor.jpg}} \\
        Trevor Cohn \\
        
        {\centering \includegraphics[scale=0.06]{dyer.jpg}} \\
        Chris Dyer\\
        
        {\centering \includegraphics[scale=0.11]{jan.jpg}} \\
        Jan Botha \\
        
        {\centering \includegraphics[scale=0.06]{olivia.jpg}} \\
        Olivia Buzek\\
        
        {\centering \includegraphics[scale=0.10]{desai.jpg}}\\
        Desai Chen\\

      \end{figure}
    \end{exampleblock}
  %\vspace{0.25in}
  \end{column}
  \begin{column}{0.7\textwidth}
    \begin{unpacked_itemize}
      \item 1:55pm Grammar induction and evaluation. Trevor
      \item 2:10pm Non-parametric models of category induction. Chris
      \item 2:25pm Inducing categories for morphology. Jan
      \item 2:35pm Smoothing, backoff and hierarchical grammars. Olivia
      \item 2:45pm Parametric models: posterior regularisation. Desai
      \item 3:00pm Break.
    \end{unpacked_itemize}
  \end{column}
\end{columns}
\end{frame}



\begin{frame}[t]{Outline}
\begin{columns}
  \begin{column}{0.2\textwidth}
    \begin{exampleblock}{}
      \begin{figure}
        \tiny
        {\centering \includegraphics[scale=0.05]{vlad.jpg}} \\
        Vlad Eidelman\\
        
        {\centering \includegraphics[scale=0.15]{ziyuan.pdf}} \\
        Ziyuan Wang\\
        
        {\centering \includegraphics[scale=0.06]{adam.jpg}} \\
        Adam Lopez\\
        
        {\centering \includegraphics[scale=0.10]{jon.pdf}} \\
        Jon Graehl\\
        
        {\centering \includegraphics[scale=0.15]{linh.pdf}} \\
        ThuyLinh Nguyen\\

      \end{figure}
    \end{exampleblock}
  \end{column}
  \begin{column}{0.7\textwidth}
    \begin{itemize}
    \setlength{\itemsep}{25pt}
    \setlength{\parskip}{0pt}
    \setlength{\parsep}{0pt}
    \item 3:15pm Training models with rich features spaces. Vlad
    \item 3:30pm Decoding with complex grammars. Adam
    \item 4:00pm Closing remarks. Phil
    \item 4:05pm Finish.
    \end{itemize}
  \end{column}
\end{columns}
\end{frame}



\begin{frame}[t]{Remember:}
  \vspace{0.5in}
  \begin{unpacked_itemize}
    \item Idea: Learn synchronous grammar labels which encode substituteability; phrases which occur in the same context should receive the same label.
    \item Result: Better models of translation structure, morphology and improved decoding algorithms.
  \end{unpacked_itemize}
\end{frame}


\begin{frame}[t]{Outline}
\begin{columns}
  \begin{column}{0.2\textwidth}
    \begin{exampleblock}{}
      \begin{figure}
        \tiny
        {\centering \includegraphics[scale=0.07]{trevor.jpg}} \\
        Trevor Cohn \\
        
        {\centering \includegraphics[scale=0.06]{dyer.jpg}} \\
        Chris Dyer\\
        
        {\centering \includegraphics[scale=0.11]{jan.jpg}} \\
        Jan Botha \\
        
        {\centering \includegraphics[scale=0.06]{olivia.jpg}} \\
        Olivia Buzek\\
        
        {\centering \includegraphics[scale=0.10]{desai.jpg}}\\
        Desai Chen\\

      \end{figure}
    \end{exampleblock}
  %\vspace{0.25in}
  \end{column}
  \begin{column}{0.7\textwidth}
    \begin{unpacked_itemize}
      \only<1>{\item \alert{1:55pm Grammar induction and evaluation. Trevor}}
      \only<2->{\item 1:55pm Grammar induction and evaluation. Trevor}
      \only<2>{\item \alert{2:10pm Non-parametric models of category induction. Chris}}
      \only<1,3->{\item 2:10pm Non-parametric models of category induction. Chris}
      \only<3>{\item \alert{2:25pm Inducing categories for morphology. Jan}}
      \only<1,2,4->{\item 2:25pm Inducing categories for morphology. Jan}
      \only<4>{\item \alert{2:35pm Smoothing, backoff and hierarchical grammars. Olivia}}
      \only<1-3,5->{\item 2:35pm Smoothing, backoff and hierarchical grammars. Olivia}
      \only<5>{\item \alert{2:45pm Parametric models: posterior regularisation. Desai}}
      \only<1-4,6->{\item 2:45pm Parametric models: posterior regularisation. Desai}
      \only<6>{\item \alert{3:00pm Break.}}
      \only<1-5>{\item 3:00pm Break.}
    \end{unpacked_itemize}
  \end{column}
\end{columns}
\end{frame}



\begin{frame}[t]{Outline}
\begin{columns}
  \begin{column}{0.2\textwidth}
    \begin{exampleblock}{}
      \begin{figure}
        \tiny
        {\centering \includegraphics[scale=0.05]{vlad.jpg}} \\
        Vlad Eidelman\\
        
        {\centering \includegraphics[scale=0.15]{ziyuan.pdf}} \\
        Ziyuan Wang\\
        
        {\centering \includegraphics[scale=0.06]{adam.jpg}} \\
        Adam Lopez\\
        
        {\centering \includegraphics[scale=0.10]{jon.pdf}} \\
        Jon Graehl\\
        
        {\centering \includegraphics[scale=0.15]{linh.pdf}} \\
        ThuyLinh Nguyen\\

      \end{figure}
    \end{exampleblock}
  \end{column}
  \begin{column}{0.7\textwidth}
    \begin{itemize}
    \setlength{\itemsep}{25pt}
    \setlength{\parskip}{0pt}
    \setlength{\parsep}{0pt}
    \only<1>{\item \alert{3:20pm Parametric models: posterior regularisation. Desai}}
    \only<2->{\item 3:20pm Parametric models: posterior regularisation. Desai}
    \only<2>{\item \alert{3:35pm Training models with rich features spaces. Vlad}}
    \only<1,3->{\item 3:35pm Training models with rich features spaces. Vlad}
    \only<3>{\item \alert{3:50pm Decoding with complex grammars. Adam}}
    \only<1,2,4->{\item 3:50pm Decoding with complex grammars. Adam}
    \only<4>{\item \alert{4:20pm Closing remarks. Phil}}
    \only<1-3,5->{\item 4:20pm Closing remarks. Phil}
    \only<5>{\item \alert{4:25pm Finish.}}
    \only<1-4>{\item 4:25pm Finish.}
    \end{itemize}
  \end{column}
\end{columns}
\end{frame}


\end{document}