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diff --git a/report/intro_slides/opening_slides.tex b/report/intro_slides/opening_slides.tex deleted file mode 100644 index 98cf4f99..00000000 --- a/report/intro_slides/opening_slides.tex +++ /dev/null @@ -1,814 +0,0 @@ -\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} |