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-\documentclass{beamer}
-
-\mode<presentation>
-{
- \usetheme{Boadilla}
- \setbeamercovered{transparent}}
-
-\usepackage[english]{babel}
-\usepackage{times}
-
-\usepackage{xcolor}
-\usepackage{colortbl}
-%\usepackage{subfigure}
-
-\usepackage{fontspec} 
-\usepackage{xunicode}
-\usepackage{xltxtra}
-\usepackage{booktabs}
-\newenvironment{CJK}{\fontspec[Scale=0.9]{PMingLiU}}{}
-\newenvironment{Geeza}{\fontspec[Scale=0.9]{Geeza Pro}}{}
-
-%% for tables
-\newcommand{\mc}{\multicolumn}
-\newcommand{\lab}[1]{\multicolumn{1}{c}{#1}}
-\newcommand{\ind}[1]{{\fboxsep1pt\raisebox{-.5ex}{\fbox{{\tiny #1}}}}}
-\newcommand{\IND}[1]{{\fboxsep1pt\raisebox{0ex}{\fbox{{\small #1}}}}}
-\newcommand\production[2]{\ensuremath{\langle\mbox{#1}, \mbox{#2}\rangle}}
-
-%% markup
-\newcommand{\buffer}[1]{{\color{blue}\textbf{#1}}}
-\newcommand{\pred}[1]{\code{#1}}
-
-%% colors
-\newcommand{\textred}[1]{\alert{#1}}
-\newcommand{\textblue}[1]{\buffer{#1}}
-\definecolor{tablecolor}{cmyk}{0,0.3,0.3,0}
-\newcommand{\keytab}[1]{\mc{1}{>{\columncolor{tablecolor}}d}{#1}}
-
-% rules
-\newcommand{\psr}[2]{#1 $\rightarrow \langle $ #2 $\rangle$}
-
-\newenvironment{unpacked_itemize}{
-\begin{itemize}
-  \setlength{\itemsep}{10pt}
-  \setlength{\parskip}{0pt}
-  \setlength{\parsep}{0pt}
-}{\end{itemize}}
-
-\newcommand{\condon}{\hspace{0pt} | \hspace{1pt}}
-\definecolor{darkblue}{rgb}{0,0,0.6}
-\newcommand{\blueexample}[1]{\textcolor{darkblue}{\rm #1}}
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-\newcommand{\ws}{\ensuremath{\vec{w}}}
-\newcommand{\pu}{\ensuremath{P_0}}
-\newcommand{\bx}{\mathbf{x}}
-\newcommand{\bz}{\mathbf{z}}
-\newcommand{\bd}{\mathbf{d}}
-\newcommand{\by}{\mathbf{y}}
-\newcommand\bleu{${B{\scriptstyle LEU}}$}
-
-
-\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[]
-%{
-%  \begin{frame}<beamer>{Outline}
-%    %\tableofcontents[currentsection,currentsubsection]
-%    \tableofcontents[currentsection]
-%  \end{frame}
-%}
-
-%\beamerdefaultoverlayspecification{<+->}
-
-\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}