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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 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[]
+%{
+%  \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)\\
+\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}{Arabic $\rightarrow$ English}
+  \begin{figure}
+    {\centering \includegraphics[scale=0.55]{arabic.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: successes}
+%\vspace{1.0cm}
+\begin{exampleblock}{Arabic $\rightarrow$ English}
+  \begin{figure}
+    {\centering \includegraphics[scale=0.55]{arabic-good.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: limitations}
+%\vspace{1.0cm}
+\begin{alertblock}{Chinese $\rightarrow$ English}
+  \begin{figure}
+    {\centering \includegraphics[scale=0.7]{chinese-bad.pdf}}
+  \end{figure}
+\end{alertblock}
+\begin{itemize}
+  \item This workshop: Learn to do it better.
+\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 The plane is faster than the train.}\\
+%   \hline
+%   Arabic   & \begin{Geeza}الطائرة أسرع من القطار\end{Geeza} \\
+%            & (the-plane) (faster) (than) (the train) \\
+%   \hline
+%   Chinese  & \begin{CJK}飞机   比   火车   快\end{CJK} \\
+%            & (plane) (compared-to) (train) (fast) \\
+%   \hline
+%     \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 systematic 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}[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}
+Systems using syntax have outperformed those that didn't:
+  \begin{center}
+    \includegraphics[scale=1.0]{ccb_graph1.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]{Impact}
+Systems using syntax have outperformed those that didn't:
+  \begin{center}
+    \includegraphics[scale=1.0]{ccb_graph2.pdf}
+  \end{center}
+\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]
+%\frametitle{Inducing a STSG given an observed tree:}
+%\only<1>{\frametitle{Inducing a STSG given an observed tree:}}
+%\only<2->{\frametitle{Existing approach (Galley et al. 2004):}}
+%
+%\begin{center}
+%  \only<1>{\hspace{1mm}\includegraphics[scale=0.45]{full_of_fun_slides_start.pdf}}
+%  \only<2>{\includegraphics[scale=0.45]{full_of_fun_slides_waligned.pdf}}
+%  \only<3>{\vspace{-2mm}\includegraphics[scale=0.45]{full_of_fun_slides_waligned_overlay.pdf}}
+%% \only<4>{\includegraphics[scale=0.4]{full_of_fun_slides_third.pdf}}
+%% \only<5>{\includegraphics[scale=0.4]{full_of_fun_slides_forth.pdf}}  
+%
+%  \only<1>{Training instance}
+%  \only<2>{Step 1: word alignment}
+%  \only<3>{Step 2: rule extraction heuristic}
+%% \only<4>{Step 2: the rules extracted}
+%% \only<5>{Step 3: estimate a grammar}
+%\end{center}
+%\end{frame}
+
+
+% Il ne veut pas travailler
+
+
+%\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 labelling system.} 
+%\item \alert{AIM: Investigate and understand the relationship between synchronous constituency and SMT performance.} 
+%\end{itemize}
+%\end{frame}
+%
+%\begin{frame}[t]{Models of translation}
+%\begin{block}{Hierarchical}
+%  \begin{center}
+%    \includegraphics[scale=0.5]{JeNeVeuxPasTravailler-Hiero-labelled.pdf}
+%    \includegraphics[scale=0.5]{IlNeVeutPasTravailler-Hiero-labelled.pdf}
+%  \end{center}
+%  \vspace{0.001in}
+%\end{block}
+%\begin{itemize}
+%\item \alert{AIM: Implement a large scale open-source synchronous constituent labelling system.} 
+%\item \alert{AIM: Investigate and understand the relationship between synchronous constituency and SMT performance.} 
+%\end{itemize}
+%\end{frame}
+
+\begin{frame}[t]{Unsupervised grammar induction}
+There has been significant research into monolingual grammar induction:
+\vspace{0.1in}
+\alert{Constituent context is a prime indicator of constituency.}
+\begin{unpacked_itemize}
+\item Alexander Clark. Unsupervised induction of stochastic context-free grammars using distributional clustering, 2001
+\item Dan Klein and Chris Manning. A Generative Constituent-Context Model for Improved Grammar Induction, 2002
+\end{unpacked_itemize}
+\vspace{0.1in}
+\alert{We can formalise this notion in algebraic structures}
+\begin{itemize}
+\item Alexander Clark. A learnable representation for syntax using residuated lattices, 2009
+\end{itemize}
+\vspace{0.1in}
+Deep connections to unsupervised word sense disambiguation, thesaurus extraction etc.
+\end{frame}
+
+%\begin{frame}[t]{Monolingual grammar induction}
+%Induce bracketing phrase-structure grammars:
+%  \includegraphics[scale=1]{klein_ccm.pdf} 
+%
+%\vspace{2ex}
+%And dependency trees: \\
+%  \includegraphics[scale=1]{klein_dependency.pdf}
+%
+%\vspace{2ex}
+%Informed by constituent context: surrounding words are a good indicator of substitutability
+%\end{frame}
+
+
+\begin{frame}[t]{SCFG Grammar Induction}
+%\vspace{1.0cm}
+\begin{exampleblock}{Distributional Hypothesis}
+\begin{quote}
+\emph{Words that occur in the same contexts tend to have similar meanings}
+\end{quote}
+\hfill (Zellig Harris, 1954)
+\end{exampleblock}
+
+\vspace{3ex}
+
+We will leverage this in a translation setting:
+\begin{itemize}
+    \item Use the contexts to \alert{cluster} translation units into groups
+    \item Units in the same group expected to be semantically and syntactically similar
+    \item Then use these cluster labels to guide translation
+    \begin{itemize}
+        \item lexical selection: translating ambiguous source word/s
+        \item reordering: consistent syntactic patterns of reordering
+    \end{itemize}
+\end{itemize}
+\end{frame}
+
+\begin{frame}[t]{Monolingual Example}
+Task: cluster words into their parts-of-speech. \\
+
+\vspace{1ex}
+Illustrate by starting with the word `deal' (noun or verb):
+
+\only<1>{\includegraphics[width=\columnwidth]{deal_first.pdf} \\ Step 1: Find contexts for `deal'}
+\only<2->{\includegraphics[width=\columnwidth]{deal.pdf} \\ Step 2: Find other words which occur in these contexts}
+%\only<3>{\includegraphics[width=\columnwidth]{deal_more.pdf} \\ \ldots continue to expand}
+
+\only<3>{
+\vspace{1ex}
+Notice that the instances of deal can be split into two connected sub-graphs:
+\begin{itemize}
+    \item noun: the left two contexts ``a \ldots with'' and ``a \ldots that''
+    \item verb: the right two contexts ``to \ldots with'' and ``not \ldots with''
+    \item neighbouring words of these contexts share the same PoS
+\end{itemize}
+}
+
+\end{frame}
+
+%\begin{frame}[t]{More Formally}
+%
+%Construct a bipartite graph
+%\begin{itemize}
+%    \item Nodes on the top layer denote word types (bilingual phrase pairs)
+%    \item Nodes on the bottom layer denote context types (monlingual/bilingual words)
+%    \item Edges connect words and their contexts
+%\end{itemize}
+%
+%\includegraphics[width=\columnwidth]{bipartite.pdf}
+%
+%\end{frame}
+
+\begin{frame}[t]{Clustering}
+
+Task is to cluster the graph into sub-graphs. Nodes in the sub-graphs should be
+\begin{itemize}
+\item strongly connected to one another
+\item weakly connected to nodes outside the sub-graph
+\item could formulate as either \emph{hard} or \emph{soft} clustering
+\end{itemize}
+Choose \alert{soft clustering} to allow for syntactic and semantic ambiguity
+
+\centering
+\includegraphics[width=0.7\columnwidth]{bipartite_lda.pdf}
+
+\end{frame}
+
+\begin{frame}[t]{Constituency and context}
+\vspace{0.25in}
+\begin{center}
+\only<1>{
+  \includegraphics[scale=0.5]{WantTo_Veux_context.pdf}
+  \includegraphics[scale=0.5]{WantTo_Veux_context2.pdf}
+}
+\only<2>{
+  \includegraphics[scale=0.5]{WantTo_Veux_context_split.pdf}
+  \includegraphics[scale=0.5]{WantTo_Veux_context2_split.pdf}
+}
+\only<3>{
+  \includegraphics[scale=0.5]{WantTo_Veux_context_split_mono.pdf}
+  \includegraphics[scale=0.5]{WantTo_Veux_context2_split_mono.pdf}
+}
+\end{center}
+\vspace{0.1in}
+%\only<1>{
+%  There has been significant research into monolingual grammar induction:
+%  \vspace{0.1in}
+%  \begin{unpacked_itemize}
+%  \item Alexander Clark. Unsupervised induction of stochastic context-free grammars using distributional clustering, 2001
+%  \item Dan Klein and Chris Manning. A Generative Constituent-Context Model for Improved Grammar Induction, 2002
+%  \end{unpacked_itemize}
+%  \alert{Constituent context is a prime indicator of constituency.}
+%}
+%\only<1>{
+\begin{unpacked_itemize}
+\item Design and apply large scale scale clustering and topic modelling algorithms (LDA, HDPs, HPYPs etc), 
+\item identify sets of frequent contexts that distinguish synchronous constituent properties.
+\item Motivated by successful models of monolingual grammar induction,
+\item deep connections to unsupervised word sense disambiguation, thesaurus extraction etc.
+\end{unpacked_itemize}
+%}
+\end{frame}
+
+\begin{frame}[t]{Latent Dirichlet Allocation (LDA)}
+
+LDA is a generative model which treats documents as bags of words
+\begin{itemize}
+    \item each word is assign a \alert{topic} (cluster tag)
+    \item words are generated from a topic-specific multinomial
+    \item topics are \alert{tied} across a document using a Dirichlet prior
+    \item $\alpha < 1$ biases towards \alert{sparse} distributions, i.e., topic reuse
+    \item inferred $\theta_d$ describes a document and $\phi_t$ describes a topic
+\end{itemize}
+
+\vspace{-3ex}
+\includegraphics[scale=0.55]{lda.pdf}
+
+\end{frame}
+
+\begin{frame}[t]{LDA over Contexts}
+
+Generative story:
+\begin{itemize}
+    \item for each word type $w$
+    \item for each of the $L$ contexts
+    \item first we draw a topic $t$, then generate the context $\vec{c}$ given the topic
+    \item the Dirichlet prior ties the topics for each $w$
+    \item we're primarily interested in the learnt $\theta$ values
+\end{itemize}
+
+\includegraphics[scale=0.4]{context_lda.pdf}
+
+\end{frame}
+
+\begin{frame}[t]{Scalable grammar extraction with MapReduce}
+\begin{itemize}
+\item Divide and conquer approach to...counting
+\begin{itemize}
+\item map function $\mathcal{M}(x) \rightarrow \langle k_1, v_1 \rangle, \langle k_2, v_2 \rangle, \ldots$
+\item write a reduce function $\mathcal{R}(k_i : v_7, v_{13} , \ldots) \rightarrow \langle k_i, \overline{v} \rangle$
+\end{itemize}
+\end{itemize}
+\begin{center}
+  \includegraphics[scale=0.4]{mroutline.pdf}
+\end{center}
+\end{frame}
+\begin{frame}[t]{Scalable grammar extraction with MapReduce : mapper}
+\begin{center}
+  \includegraphics[scale=0.4]{mapper.pdf}
+\end{center}
+\end{frame}
+
+\begin{frame}[t]{Scalable grammar extraction with MapReduce : reducer}
+\begin{center}
+  \includegraphics[scale=0.4]{reducer.pdf}
+\end{center}
+\end{frame}
+
+\begin{frame}[t]{Scalable grammar extraction with MapReduce : Hadoop}
+\begin{center}
+  \includegraphics[scale=0.4]{hadoop-extract.pdf}
+\end{center}
+\end{frame}
+
+\begin{frame}[t]{Scalable grammar extraction with MapReduce : Hadoop}
+\begin{center}
+  \includegraphics[scale=0.4]{hadoop-extract-arrows.pdf}
+\end{center}
+\end{frame}
+
+
+%\begin{frame}[t]{Discriminative training}
+%\begin{unpacked_itemize}
+%\item MIRA
+%\item Expected loss minimisation.
+%\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]{Draft Schedule}
+%\begin{itemize}
+%\item Pre-workshop:
+%  \begin{itemize}
+%  \item Collect existing open-source tools for synchronous grammar induction,
+%  \item Collect corpora across a range of translations conditions: small, large, low-density languages etc.
+%  \item Implement phrase and context extraction algorithms.
+%  \item Design the integration of various existing approaches into the decoders.
+%  \end{itemize}
+%\item Week 1:
+%  \begin{itemize}
+%  \item Optimise and reconfigure decoders to handle labelled synchronous grammars,
+%  \item Perform a empirical study of synchronous constituency models.
+%  \end{itemize}
+%\end{itemize}
+%\end{frame}
+
+%\begin{frame}[t]{Draft Schedule}
+%\begin{itemize}
+%\item Week 2-3:
+%  \begin{itemize}
+%  \item Continue optimising decoder to handle labelled synchronous grammars,
+%  \item Implement unsupervised label induction algorithms, initially inducing a single label per-phrase.
+%  \item Extend to ''topic"-modelling style representation where a phrase may have multiple labellings.
+%  \item Perform experimental comparison of existing synchronous grammar translation models.
+%  \end{itemize}
+%\item Week 3-6:
+%  \begin{itemize}
+%  \item Perform experimental comparison of unsupervised synchronous grammar translation models.
+%  \item Extend the evaluation to small/big data sets, hi-density vs. low-density language pairs.
+%  \item Create ``semi-supervised'' models combining knowledge from treebank parser into the unsupervised algorithms.
+%  \item Wrap-up and write final report.
+%  \end{itemize}
+%\end{itemize}
+%\end{frame}
+
+
+\begin{frame}[t]{Pre-workshop experiments}
+\vspace{0.25in}
+We have implemented a baseline constituent modelling and distrbuted grammar extraction pipeline. Initial results on the small BTEC corpora:
+
+\vspace{0.25in}
+\begin{exampleblock}
+\footnotesize
+\centering
+\begin{tabular}{lcccccc}
+\toprule
+Categories & \small 1-gram & \small 2-grams & \small 3-grams & \small 4-grams & \small BP  &  BLEU  \\
+\midrule                                                                                            
+1          & \small 84.7 & \small 62.0    & \small 47.2    & \small 36.4    & \small 0.969 &  \textcolor{blue}{53.10} \\
+10         & \small 84.0 & \small 60.9    & \small 46.4    & \small 35.9    & \small 0.979 &  \textcolor{red}{52.88} \\
+25         & \small 84.4 & \small 61.8    & \small 47.6    & \small 36.7    & \small 0.973 &  \textcolor{blue}{53.47} \\
+50         & \small 84.8 & \small 61.2    & \small 46.6    & \small 36.2    & \small 0.971 &  \textcolor{red}{52.83} \\
+100        & \small 83.5 & \small 60.1    & \small 45.7    & \small 35.3    & \small 0.972 &  \textcolor{red}{51.86} \\
+\bottomrule
+\end{tabular}
+\end{exampleblock}
+\end{frame}
+
+
+%{\centering
+%A unique opportunity to bring together researchers operating at the coal face of SMT development with leading theoreticians in the field of formal grammar induction.
+%}
+%\begin{unpacked_itemize}
+%\item Understand the relationship between constituent labels and performance in SMT,
+%\item Compare monolingual and bilingual induced grammars against parser output in terms of translation quality,
+%\item Produce a large scale implementation of the label induction algorithms,
+%\end{unpacked_itemize}
+%\begin{unpacked_itemize}
+%\item \alert{Learn language-pair dependent structure that produces translation performance gains across all language pairs,}
+%\item \alert{Initiate a research program that redirects the SMT research community back to language neutral unsupervised systems.}
+%\end{unpacked_itemize}
+
+
+\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}