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a/report/intro_slides/agenda.tex b/report/intro_slides/agenda.tex deleted file mode 100644 index 13f952b8..00000000 --- a/report/intro_slides/agenda.tex +++ /dev/null @@ -1,32 +0,0 @@ -\documentclass[12pt]{article} -\usepackage{times} -\usepackage[applemac]{inputenc} - -\makeatletter - -\title{CLSP Workshop 2010: \\ Pre-Workshop Meeting} -\date{24 May, 2010} - -\begin{document} -\maketitle - -\centering -\subsection*{Attendees} -Phil Blunsom, Adam Lopez, Chris Dyer, Jonathen Graehl, Vladimir Eidelman, Ziyuan Wang, Olivia Buzek, Desai Chen. - -\subsection*{Apologies} -Trevor Cohn, Jan Botha. - -\section*{Agenda} -\begin{enumerate} -\centering -\item Opening welcome and introductions -\item Presentation: Workshop overview and goals -\item Undergraduate student mentoring -\item Preliminary graduate student goals -\item Draft workshop work plan -\item Pre-workshop tasks -\item JHU accounts and resources -\end{enumerate} - -\end{document} diff --git a/report/intro_slides/arabic-good.graffle 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-\newcommand\bleu{\textsc{Bleu}} -\newcommand\giza{{GIZA++}} -\newcommand\boe{\mathbf{e}} -\newcommand\bof{\mathbf{f}} -\newcommand{\ind}[1]{{\fboxsep1pt\raisebox{-.5ex}{\fbox{{\tiny #1}}}}} -% allows margin notes -%\newcommand\margin[1]{} % hide them!!! -\newcommand\margin[1]{\mbox{}\marginpar{\raggedright\hspace{0pt}\tiny\em#1}} -\addtolength{\marginparwidth}{-3ex} - -\newcommand{\ts}{\ensuremath{\vec{t}}} -\newcommand{\es}{\ensuremath{\vec{e}}} -\newcommand{\rs}{\ensuremath{\vec{r}}} -\newcommand{\condon}{\hspace{0pt} | \hspace{1pt}} -\newcommand{\but}[2]{\ensuremath{{\bf #1}_{-#2}}} -\newcommand{\blt}[2]{\ensuremath{{\bf #1}_{<#2}}} -\newcommand{\pu}{\ensuremath{P_0}} -\renewcommand{\vec}[1]{{\bf #1}} -\newcommand{\emin}{\ensuremath{\es^-}} - -\newcommand{\bibsnip}{\vspace{-1ex}} - -%% sloppy linebreaks -%\sloppy - -%% no extra spacing after dots -%\frenchspacing - -\makeatletter -\setlength{\columnsep}{3mm} % this one's nasty!!! - -%\setlength{\topmargin}{0in} -%\setlength{\headheight}{0in} -%\setlength{\headsep}{-0.2in} -%\setlength{\textheight}{10.0in} -%\setlength{\oddsidemargin}{0in} -%\setlength{\textwidth}{6.5in} -\setlength\titlebox{1.65in} -\renewcommand{\baselinestretch}{0.96} -\setlength{\textfloatsep}{3mm} -\setlength{\floatsep}{1.5mm} - - -\title{Unsupervised models of Synchronous Grammar Induction for SMT} -\author{ - Phil Blunsom\\ - %\vspace{6pt} - Phil.Blunsom@comlab.ox.ac.uk\\ -% {\rm $^*$Computing Laboratory}\\ - University of Oxford\\ - \And - Alex Clark\\ - %\vspace{6pt} - alexc@cs.rhul.ac.uk\\ -% {\rm $^\textrm{\textdagger}$Department of Computer Science}\\ - Royal Holloway University - \And - Trevor Cohn\\ - %\vspace{6pt} - T.Cohn@dcs.shef.ac.uk\\ -% {\rm $^\textrm{\textdagger}$Department of Computer Science}\\ - University of Shefield -\\ - \AND - Chris Dyer\\ - %\vspace{6pt} - redpony@umd.edu\\ -% {\rm $^\textrm{\textdagger}$Department of Computer Science}\\ - University of Maryland - \And - Adam Lopez\\ - %\vspace{6pt} - alopez@inf.ed.ac.uk\\ -% {\rm $^\textrm{\textdagger}$Department of Computer Science}\\ - University of Edinburgh -} - -\begin{document} -\maketitle - -\begin{abstract} -Synchronous grammar transducers have made a big impact in the field of machine translation in recent years. -However, current techniques for acquiring translation grammars are either overly simplistic or else too heavily reliant on the availability of high-quality statistical parsers. -We propose the development of algorithms for large-scale unsupervised synchronous grammar induction. -Using advanced clustering techniques from machine learning we aim to induce grammars which use a rich set of non-terminals, %, in contrast to many existing approaches which have only one. -thereby producing high quality translation models for language pairs solely using a parallel corpus. %, without relying on treebank parsers. -\end{abstract} - -%%Research into statistical machine translation has changed dramatically in recent years with the introduction of grammar-based transducers (e.g., Chang's Hiero and USC/ISI's syntax-based system). -%A critical component of synchronous grammar translation models is their \emph{grammar} which encodes translational equivalence and licenses reordering between tokens in the source and target languages. -%There is considerable scope for improving beyond current techniques for automatically acquiring synchronous grammars from bilingual corpora, which seek to find either extremely simple grammars with only one non-terminal or else rely on treebank-trained parsers. -%The simple grammars are incapable of representing constituency information, while the richer grammars limit the systems' portability to new target languages while enforcing a restrictive notion of linguistic constituency. -%Instead we propose to develop an unsupervised method for large-scale unsupervised synchronous grammar induction using multiple non-terminal symbols to represent contextual information. -%In such a way we can harness the benefits of a richer grammar without the limiting data requirements or linguistic constraints. - -The last decade of research in Statistical Machine Translation (SMT) has seen rapid progress. %with small scale research systems maturing into large commercial products and ubiquitous online tools. %\footnote{e.g., translate.google.com, www.systran.co.uk, www.languageweaver.com} -Unfortunately the success of SMT has not been uniform; -%current state-of-the-art translation output varies markedly in quality depending on the languages being translated. -%Those language pairs that are -closely related language pairs -%(e.g., English and French) -can be translated with a high degree of precision, while for distant pairs -%(e.g., English and Chinese) -the result is far from acceptable. -It is tempting to argue that SMT's current limitations can be overcome simply by increasing the amount of data on which the systems are trained. -However, large scale evaluation campaigns for Chinese~$\rightarrow$~English translation have not yielded the expected gains despite the increasing size of the models. -%While many researchers are tackling these issues, their proposed solutions are limited by focusing on more expressive models of translation rather than addressing the issue of how, and what, translation units are learnt a priori. - -Those models which have been most successful for translating between structurally divergent language pairs have been based on synchronous tree substitution grammars (STSG). -%However these successes have been tempered by a reliance on high quality monolingual parse trees on the output side of the translation model, severely restricting the applicability of such models to the case of translating into English. -A critical component of these translation models is their \emph{grammar} which encodes translational equivalence and licenses reordering between tokens in the source and target languages. -There is considerable scope for improving beyond current techniques for automatically acquiring synchronous grammars from bilingual corpora, which seek to find either extremely simple grammars with only one non-terminal or else rely on treebank-trained parsers. -The simple grammars are incapable of representing the substitutability of a constituent, while the richer grammars limit the systems' portability to new target languages while enforcing a restrictive notion of linguistic constituency (Figure \ref{fig:models}). -%A further, but more subtle, limitation of these models is the assumption that the particular brand of linguistic structure assigned by a parser (usually a form of phrase structure grammar learnt from the Penn. Treebank) is predominantly isomorphic to that of the input language; an assumption which is rarely true for distantly related language pairs (or even closely related ones: Figure \ref{fig:models}). - -Clearly there is a need for research into the unsupervised induction of synchronous grammar based translation models. -Previous research has focussed on structured learning approaches requiring costly global inference over translation pair derivations, limiting the ability of these models to be scaled to large datasets. -Here we propose the more pragmatic approach of embracing existing algorithms for inducing unlabelled SCFGs (e.g. the popular Hiero model) as a starting point, and then using state-of-the-art clustering and topic modelling algorithms to independently learn syntactic congruence classes for translation rules in the grammar. - -\begin{figure} - \subfigure[Formal syntax]{\includegraphics[scale=0.34]{JeNeVeuxPasTravailler-hiero.pdf}} - \subfigure[Linguistic syntax]{\includegraphics[scale=0.34]{JeNeVeuxPasTravailler-tsg.pdf}} -\caption{What is the best syntactic structure for SMT?} -\label{fig:models} -\end{figure} - -The agenda of the workshop will be motivated by two goals: (1) to investigate the role of constituency and syntactic congruence in synchronous grammars; (2) to develop scalable algorithms for assigning labels to translation rules in Hiero style grammars. These algorithms will be implemented within the Joshua decoder. - -\paragraph{1) Synchronous constituency} -The first goal deals with the question of the degree to which practical synchronous grammar SMT systems should be based on linguistic or non-linguistic models of syntax. -Previous work has assumed a linguistic motivation, but there is a compelling argument that a more appropriate representation may be one which specifically addresses the divergence between a given language pair (Figure~\ref{fig:models}). -Such a grammar might simply encode the number of tokens in the constituent being generated, the number of reordering operations on the path from the root to that constituent, or whether a constituent prefers to reorder in a specific direction. - -\paragraph{2) Clustering translation units} -The second goal will be to adapt existing unsupervised clustering techniques, such as Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Processes (HDPs), to the task of assigning equivalence classes to phrase translations. -Inspired by work in monolingual PCFG learning, we will investigate generative models which describe the production of phrase translations in terms of sequences of tokens (or word classes) and their observed contexts. - -By investigating these goals in the context of a CLSP workshop we hope to both generate interest and further research into widely applicable unsupervised techniques for synchronous grammar induction, and to provide a benchmark implementation ready for wide adoption within the SMT research community. -\end{document} diff --git a/report/intro_slides/context_lda.pdf b/report/intro_slides/context_lda.pdf deleted file mode 100644 index 0537f704..00000000 Binary files a/report/intro_slides/context_lda.pdf and /dev/null differ diff --git a/report/intro_slides/deal.pdf b/report/intro_slides/deal.pdf deleted file mode 100644 index fd92787a..00000000 Binary files a/report/intro_slides/deal.pdf and /dev/null differ diff --git a/report/intro_slides/deal_first.pdf b/report/intro_slides/deal_first.pdf deleted file mode 100644 index 59049c22..00000000 Binary files a/report/intro_slides/deal_first.pdf and /dev/null differ diff --git a/report/intro_slides/deal_more.pdf b/report/intro_slides/deal_more.pdf deleted file mode 100644 index 434a011a..00000000 Binary files a/report/intro_slides/deal_more.pdf and /dev/null differ diff --git a/report/intro_slides/deal_numbers.pdf b/report/intro_slides/deal_numbers.pdf deleted file mode 100644 index c16af67d..00000000 Binary files a/report/intro_slides/deal_numbers.pdf and /dev/null differ diff --git a/report/intro_slides/desai.jpg b/report/intro_slides/desai.jpg deleted file mode 100644 index 1c64307f..00000000 Binary files a/report/intro_slides/desai.jpg and /dev/null differ diff --git a/report/intro_slides/dyer.jpg b/report/intro_slides/dyer.jpg deleted file mode 100644 index 382cf56c..00000000 Binary files a/report/intro_slides/dyer.jpg and /dev/null differ diff --git a/report/intro_slides/final_slides.pdf b/report/intro_slides/final_slides.pdf deleted file mode 100644 index cb842396..00000000 Binary files a/report/intro_slides/final_slides.pdf and /dev/null differ diff --git a/report/intro_slides/final_slides.tex b/report/intro_slides/final_slides.tex deleted file mode 100644 index a73481ac..00000000 --- a/report/intro_slides/final_slides.tex +++ /dev/null @@ -1,783 +0,0 @@ -\documentclass{beamer} - -\mode -{ - \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}{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} diff --git a/report/intro_slides/full_of_fun_hiero.graffle b/report/intro_slides/full_of_fun_hiero.graffle deleted file mode 100644 index 4dde7f4e..00000000 --- a/report/intro_slides/full_of_fun_hiero.graffle +++ /dev/null @@ -1,2788 +0,0 @@ - - - - - ActiveLayerIndex - 0 - AutoAdjust - - CanvasColor - - w - 1 - - CanvasOrigin - {0, 0} - CanvasScale - 1 - ColumnAlign - 1 - ColumnSpacing - 36 - CreationDate - 2009-04-22 17:41:04 +0100 - Creator - Trevor Cohn - DisplayScale - 1 cm = 1 cm - GraphDocumentVersion - 5 - GraphicsList - - - Class - LineGraphic - Head - - ID - 467 - - ID - 477 - Points - - {454.08, 412.606} - {426.153, 313.083} - - Style - - stroke - - HeadArrow - 0 - TailArrow - 0 - - - Tail - - ID - 242 - - - - Class - LineGraphic - Head - - ID - 205 - - ID - 473 - Points - - {431.29, 313.083} - {519.469, 412.727} - - Style - 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-\mode -{ - \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}{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. - 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-\mode -{ - \usetheme{Boadilla} - \setbeamercovered{transparent}} - -\usepackage[english]{babel} -\usepackage{times} - -\usepackage{xcolor} -\usepackage{colortbl} -%\usepackage{subfigure} - -%% 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{Clustering of phrases and contexts} -\author{Trevor Cohn} -\date{\today} - -\begin{document} - -\begin{frame}[t]{Motivation} -%\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]{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} - -\end{document} diff --git a/report/intro_slides/treebanks.tex b/report/intro_slides/treebanks.tex deleted file mode 100644 index ed416031..00000000 --- a/report/intro_slides/treebanks.tex +++ /dev/null @@ -1,11 +0,0 @@ -\begin{table} - \begin{tabular}{l|rrr} - \hline - Language & Words & Domain & Source \\ \hline - Arabic & 300K (1M planned) & News & AFP newswire \\ - Chinese & 0.5M & Broadcasting news & Xinhua newswire, Sinorama and HK news \\ - English & 4.5M& Financial news & Wall Street Journal \\ - Korean & 54K & Military & Military language training manuals \\ \hline - \end{tabular} - \caption{Major treebanks: data size and domain \label{table_treebanks_size}} -\end{table} diff --git a/report/intro_slides/trevor.jpg b/report/intro_slides/trevor.jpg deleted file mode 100644 index ddee5c4a..00000000 Binary files a/report/intro_slides/trevor.jpg and /dev/null differ diff --git a/report/intro_slides/urdu-25hp.graffle b/report/intro_slides/urdu-25hp.graffle deleted file mode 100644 index efd1761b..00000000 --- a/report/intro_slides/urdu-25hp.graffle +++ /dev/null @@ -1,1053 +0,0 @@ - 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-%% sloppy linebreaks -%\sloppy - -%% no extra spacing after dots -%\frenchspacing - -\makeatletter -\setlength{\columnsep}{3mm} % this one's nasty!!! - -%\setlength{\topmargin}{0in} -%\setlength{\headheight}{0in} -%\setlength{\headsep}{-0.2in} -\setlength{\textheight}{10.0in} -%\setlength{\oddsidemargin}{0in} -%\setlength{\textwidth}{6.5in} -\setlength\titlebox{1.0in} -\renewcommand{\baselinestretch}{0.965} -\setlength{\textfloatsep}{3mm} -\setlength{\floatsep}{1.5mm} - - -\title{Models of Synchronous Grammar Induction for SMT} -\author{ - Phil Blunsom\\ - %\vspace{6pt} - Phil.Blunsom@comlab.ox.ac.uk\\ -% {\rm $^*$Computing Laboratory}\\ - University of Oxford\\ -% \And -% Alex Clark\\ -% %\vspace{6pt} -% alexc@cs.rhul.ac.uk\\ -%% {\rm $^\textrm{\textdagger}$Department of Computer Science}\\ -% Royal Holloway University -% \And -% Trevor Cohn\\ -% %\vspace{6pt} -% T.Cohn@dcs.shef.ac.uk\\ -%% {\rm $^\textrm{\textdagger}$Department of Computer Science}\\ -% University of Shefield -%\\ -% \AND -% Chris Dyer\\ -% %\vspace{6pt} -% redpony@umd.edu\\ -%% {\rm $^\textrm{\textdagger}$Department of Computer Science}\\ -% University of Maryland -% \And -% Adam Lopez\\ -% %\vspace{6pt} -% alopez@inf.ed.ac.uk\\ -%% {\rm $^\textrm{\textdagger}$Department of Computer Science}\\ -% University of Edinburgh -} - -\begin{document} -\maketitle - -%\begin{abstract} -%Synchronous grammar transducers have made a big impact in the field of machine translation in recent years. -%However, current techniques for acquiring translation grammars are either overly simplistic or else too heavily reliant on the availability of high-quality statistical parsers. -%We propose the development of algorithms for large-scale unsupervised synchronous grammar induction. -%Using advanced clustering techniques from machine learning we aim to induce grammars which use a rich set of non-terminals, %, in contrast to many existing approaches which have only one. -%thereby producing high quality translation models for language pairs solely using a parallel corpus. %, without relying on treebank parsers. -%\end{abstract} - -%%Research into statistical machine translation has changed dramatically in recent years with the introduction of grammar-based transducers (e.g., Chang's Hiero and USC/ISI's syntax-based system). -%A critical component of synchronous grammar translation models is their \emph{grammar} which encodes translational equivalence and licenses reordering between tokens in the source and target languages. -%There is considerable scope for improving beyond current techniques for automatically acquiring synchronous grammars from bilingual corpora, which seek to find either extremely simple grammars with only one non-terminal or else rely on treebank-trained parsers. -%The simple grammars are incapable of representing constituency information, while the richer grammars limit the systems' portability to new target languages while enforcing a restrictive notion of linguistic constituency. -%Instead we propose to develop an unsupervised method for large-scale unsupervised synchronous grammar induction using multiple non-terminal symbols to represent contextual information. -%In such a way we can harness the benefits of a richer grammar without the limiting data requirements or linguistic constraints. - -The last decade of research in Statistical Machine Translation (SMT) has seen rapid progress. %with small scale research systems maturing into large commercial products and ubiquitous online tools. %\footnote{e.g., translate.google.com, www.systran.co.uk, www.languageweaver.com} -Unfortunately these successes have not been uniform; -%current state-of-the-art translation output varies markedly in quality depending on the languages being translated. -%Those language pairs that are -closely related language pairs -%(e.g., English and French) -can be translated with a high degree of precision, while for distant pairs -%(e.g., English and Chinese) -the result is far from acceptable. -%It is tempting to argue that SMT's current limitations can be overcome simply by increasing the amount of data on which the systems are trained. -%However, large scale evaluation campaigns for Chinese~$\rightarrow$~English translation have not yielded the expected gains despite the increasing size of the models. -%While many researchers are tackling these issues, their proposed solutions are limited by focusing on more expressive models of translation rather than addressing the issue of how, and what, translation units are learnt a priori. - -Models which have been most successful for translating between structurally divergent language pairs have been based on synchronous grammars. -%However these successes have been tempered by a reliance on high quality monolingual parse trees on the output side of the translation model, severely restricting the applicability of such models to the case of translating into English. -A critical component of these translation models is their \emph{grammar} which encodes translational equivalence and licenses reordering between tokens in the source and target languages. -There is considerable scope for improving beyond current techniques for automatically acquiring synchronous grammars from bilingual corpora, which seek to find either extremely simple grammars with only one non-terminal or else rely on treebank-trained parsers. -The simple grammars are incapable of representing the substitutability of a constituent, while the richer grammars limit the systems' portability to new target languages (effectively limiting us to translating into/out of English) while enforcing a restrictive notion of linguistic constituency (Figure \ref{fig:models}). -%A further, but more subtle, limitation of these models is the assumption that the particular brand of linguistic structure assigned by a parser (usually a form of phrase structure grammar learnt from the Penn. Treebank) is predominantly isomorphic to that of the input language; an assumption which is rarely true for distantly related language pairs (or even closely related ones: Figure \ref{fig:models}). - -Clearly there is a need for research into the unsupervised induction of synchronous grammar based translation models. -%Previous research has focussed on structured learning approaches requiring costly global inference over translation pair derivations, limiting the ability of these models to be scaled to large datasets. -We propose the pragmatic approach of embracing existing algorithms for inducing unlabelled SCFGs (e.g. the popular Hiero model), and then using state-of-the-art hierarchical non-parametric Bayesian models to independently learn syntactic classes for translation rules in the grammar. - - -The agenda of the workshop will address two goals: (1) to implement and systematically investigate the performance of current synchronous grammar based SMT systems, focusing on the role of constituency and syntactic classes in informing translation structure; (2) to develop scalable unsupervised algorithms for assigning labels to translation rules in synchronous grammars. -These algorithms will be implemented within the {\em Joshua} decoder with the intention of providing an open source implementation of the existing and proposed synchronous grammar SMT systems. - -\paragraph{1) Systematic comparison of synchronous grammar based SMT} -Firstly we will extend the Joshua decoder to handle a range of the currently proposed synchronous grammar based SMT models. -Through experimentation on a range of parallel corpora (small and large, hi and low density languages), we will systematically explore the question of what the most effective models of synchronous syntax for SMT are. -%Previous work has assumed a linguistic motivation, but there is a compelling argument that a more appropriate representation may be one which specifically addresses the divergence between a given language pair (Figure~\ref{fig:models}). -%Such a grammar might simply encode the number of tokens in the constituent being generated, the number of reordering operations on the path from the root to that constituent, or whether a constituent prefers to reorder in a specific direction. - -\paragraph{2) Unsupervised learning of labelled synchronous grammars} -The second goal will be to apply unsupervised Bayesian techniques, such as Latent Dirichlet Allocation (LDA) and hierarchical non-parametric models (HDPs, HPYPs), to the task of assigning equivalence classes to phrase translations. -Inspired by work in monolingual PCFG learning, we will investigate generative models which describe the production of phrase translations in terms of sequences of tokens (or word classes) and their observed contexts. - -We have put together an enviable superset of faculty and graduate students from which the senior members of the workshop will be drawn: Joy Ying Zhang (CMU), Trevor Cohn (Sheffield), Alex Clark (RHUL), Chris Dyer (UMD), Adam Lopez (Edinburgh), Yang Liu (CAS), Zhifei Li (JHU) and Andreas Zollmann (CMU). - -The success of the workshop will impact widely on both the machine translation community, and the field of grammar induction. -By investigating our stated goals in the context of a CLSP workshop we will provide a deep understanding of synchronous grammar based SMT; both generating interest and furthering research into widely applicable unsupervised techniques for synchronous grammar induction. -Such techniques promise to bring high performance SMT models, only currently applicable to working with English, to the full range of languages of the world. -In addition we will provide a benchmark implementation of synchronous grammar based SMT models ready for wide adoption within the SMT research community. - -\begin{figure} -% \subfigure[Formal syntax]{\includegraphics[scale=0.2]{JeNeVeuxPasTravailler-Hiero.pdf}} - \subfigure[Unsupervised syntax]{\includegraphics[scale=0.36]{JeNeVeuxPasTravailler-hiero-labelled.pdf}} - \subfigure[Treebank syntax]{\includegraphics[scale=0.36]{JeNeVeuxPasTravailler-tsg.pdf}} -\caption{What is the best syntactic structure for SMT?} -\label{fig:models} -\end{figure} -\end{document} diff --git a/report/intro_slides/wsj23_example.pdf b/report/intro_slides/wsj23_example.pdf deleted file mode 100644 index 860fc83d..00000000 Binary files a/report/intro_slides/wsj23_example.pdf and /dev/null differ diff --git a/report/intro_slides/ziyuan.pdf b/report/intro_slides/ziyuan.pdf deleted file mode 100644 index 43c9efd2..00000000 Binary files a/report/intro_slides/ziyuan.pdf and /dev/null differ -- cgit v1.2.3