Part way through slides.

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@430 ec762483-ff6d-05da-a07a-a48fb63a330f

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diff --git a/report/intro_slides/final_slides.tex b/report/intro_slides/final_slides.tex
index 521dc003..753f4138 100644
--- 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}