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diff --git a/report/intro_slides/final_slides.tex b/report/intro_slides/final_slides.tex index 753f4138..164fde36 100644 --- a/report/intro_slides/final_slides.tex +++ b/report/intro_slides/final_slides.tex @@ -75,7 +75,7 @@ % $^3$Carnegie Mellon University\\ % $^4$University of Edinburgh } -\date[June 21]{June 21, 2010} +\date[June 28]{June 28, 2010} %\subject{Unsupervised models of Synchronous Grammar Induction for SMT} @@ -109,26 +109,27 @@ %\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]{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} @@ -158,7 +159,7 @@ \end{itemize} \end{frame} -\begin{frame}[t]{Statistical machine translation: Before} +\begin{frame}[t]{Statistical machine translation: state-of-the-art} %\vspace{1.0cm} \begin{exampleblock}{Urdu $\rightarrow$ English} \begin{figure} @@ -170,18 +171,6 @@ \end{itemize} \end{frame} -\begin{frame}[t]{Statistical machine translation: After} -%\vspace{1.0cm} -\begin{exampleblock}{Urdu $\rightarrow$ English} - \begin{figure} - {\centering \includegraphics[scale=0.55]{urdu-25hp.pdf}} - \end{figure} -\end{exampleblock} -\begin{itemize} - \item In this workshop we've made some small steps towards better translations for difficult language pairs. -\end{itemize} -\end{frame} - \begin{frame}[t]{Statistical machine translation: limitations} \vspace{1.0cm} @@ -232,9 +221,9 @@ \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?)} + \item Phrasal translation equivalences + \item Constituent reordering + \item Morphology \end{itemize} } \end{frame} @@ -245,50 +234,6 @@ \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 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} \frametitle{Using syntax in Machine Translation:} \footnotesize @@ -325,6 +270,60 @@ Output: \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{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{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]{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} @@ -363,6 +362,23 @@ Output: \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} @@ -381,55 +397,33 @@ Output: %\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]{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]{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: @@ -449,88 +443,338 @@ We will predominately evaluate using BLEU, but also use automatic structured met \begin{frame}[t]{Workshop Streams} +Expand, describing challenges faced in each stream. \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. +\item Improve SCFG decoders to efficiently handle the grammars produce. +\item Investigate discriminative training regimes to leverage features extracted from these grammars. \end{unpacked_itemize} \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]{Language pairs (small)} +\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: 52.4 (16 references) - \item Prospects: BTEC always gives you good results + \item Hiero baseline score: 57.0 (16 references) \end{itemize} \item NIST Urdu-English: \begin{itemize} \item 50k sentence pairs - \item Hiero baseline score: MT05 - 23.7 (4 references) + \item Hiero baseline score: 21.1 (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 100k 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: ??? + \item Major challenges: V2 / V-final word order, morphology \end{itemize} -\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 Experimental Setup. 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} + \vspace{0.25in} + \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]{This slide is intentionally left blank.} +\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 Experimental Setup. Trevor}} + \only<2->{\item 1:55pm Experimental Setup. 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]{Summary} + +\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} + \vspace{0.25in} + \end{column} + \begin{column}{0.7\textwidth} + \begin{itemize} + \setlength{\itemsep}{25pt} + \setlength{\parskip}{0pt} + \setlength{\parsep}{0pt} + \only<1>{\item \alert{3:15pm Training models with rich features spaces. Vlad}} + \only<2->{\item 3:15pm Training models with rich features spaces. Vlad} + \only<2>{\item \alert{3:30pm Decoding with complex grammars. Adam}} + \only<1,3->{\item \alert{4:00pm Closing remarks. Phil} + \only<3>{\item 4:05pm Finish.} + \only<>{\item 4:05pm Finish.} + \end{itemize} + \end{column} +\end{columns} +\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 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} + \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: our unsupervised grammars} +%\vspace{1.0cm} +\begin{exampleblock}{Urdu $\rightarrow$ English} + \begin{figure} + {\centering \includegraphics[scale=0.55]{urdu-25hp.pdf}} + \end{figure} +\end{exampleblock} +\begin{itemize} + \item In this workshop we've made some small steps towards better translations for difficult language pairs. +\end{itemize} +\only<2->{ + Google Translate: \\ + \hspace{0.25in} {\em *After the attack a number of local residents has blank areas.} +} +\end{frame} + + +\begin{frame}[t]{Induced Translation Structure} +\begin{center} +\includegraphics[scale=0.32]{joshua_tree19.pdf} +\end{center} +\end{frame} + +\begin{frame}[t]{What we've achieved:} + \vspace{0.5in} + \begin{unpacked_itemize} + \item + \item + \end{unpacked_itemize} +\end{frame} + + +\begin{frame}[t]{We're we'll go from here:} + \vspace{0.5in} + \begin{unpacked_itemize} + \item + \item + \end{unpacked_itemize} +\end{frame} + + \end{document} |