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authorphilblunsom@gmail.com <philblunsom@gmail.com@ec762483-ff6d-05da-a07a-a48fb63a330f>2010-07-26 23:35:33 +0000
committerphilblunsom@gmail.com <philblunsom@gmail.com@ec762483-ff6d-05da-a07a-a48fb63a330f>2010-07-26 23:35:33 +0000
commitcd24c0b69bbfe3218fc8ad50f0729f388b23cc2a (patch)
tree287ac0b0f9be4cbafb2c6fb60264b7eb4e4d26d6 /report/intro_slides/final_slides.tex
parente76460b3d35dd486e6d53ed5b401af4ca79bf410 (diff)
Part way through slides.
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--- a/report/intro_slides/final_slides.tex
+++ b/report/intro_slides/final_slides.tex
@@ -136,7 +136,7 @@
%\vspace{1.0cm}
\begin{exampleblock}{Urdu $\rightarrow$ English}
\begin{figure}
- {\centering \includegraphics[scale=0.55]{urdu-bw.pdf}}
+ {\centering \includegraphics[scale=0.55]{urdu.pdf}}
\end{figure}
\vspace{0.10cm}
\end{exampleblock}
@@ -458,272 +458,6 @@ We will predominately evaluate using BLEU, but also use automatic structured met
\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}
@@ -770,79 +504,6 @@ Generative story:
\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}