Promoted the SCFG section to a chapter, will add more on SAMT shortly

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@562 ec762483-ff6d-05da-a07a-a48fb63a330f

3 files changed, 47 insertions, 47 deletions

diff --git a/report/SCFGs.tex b/report/SCFGs.tex
new file mode 100644
index 00000000..0002405d
--- /dev/null
+++ b/report/SCFGs.tex
@@ -0,0 +1,46 @@
+
+\chapter{Synchronous context free grammars} \label{sec:scfg}
+
+%\subsubsection*{Synchronous context free grammar} \label{sec:scfg}
+\begin{figure}[t]
+\begin{center}
+\includegraphics[width=0.6\columnwidth]{example_derivation2.pdf}
+\end{center}
+\caption[Derivation]{An example SCFG derivation from a Chinese source sentence which yields the English sentence: {\em ``Brown arrived in Shanghai from Beijing late last night.''}. The non-terminal alignment $\mathbf{a}$ is specified by the variable subscripts.}
+\label{fig:intro_example_derivation}
+\end{figure}
+
+The translation models discussed explored in this workshop are based on synchronous grammars. 
+Here we provide a short definition of the formalism we've employed: synchronous context free grammar (SCFG).
+
+A synchronous context free grammar (SCFG, \cite{lewis68scfg}) generalizes context-free grammars to generate strings concurrently in two (or more) languages. A string pair is generated by applying a series of paired rewrite rules of the form, $X \rightarrow \langle \mathbf{e}, \mathbf{f}, \mathbf{a} \rangle$, where $X$ is a non-terminal, $\mathbf{e}$ and $\mathbf{f}$ are strings of terminals and non-terminals and $\mathbf{a}$ specifies a one-to-one alignment between non-terminals in $\mathbf{e}$ and $\mathbf{f}$.
+In the context of SMT, by assigning the source and target languages to the respective sides of a probabilistic SCFG it is possible to describe translation as the process of parsing the source sentence, which induces a parallel tree structure and translation in the target language \cite{chiang07hierarchical}.  
+Terminal are rewritten as pairs of strings of terminal symbols in the source and target languages.  Additionally, one side of a terminal expansion may be the special symbol $\epsilon$, which indicates a null alignment which permits arbitrary insertions and deletions.
+Figure \ref{fig:intro_example_derivation} is an example derivation for Chinese to English translation using an SCFG of the form that I propose to learn using non-parametric Bayesian models.
+
+The generative story is as follows. 
+In the beginning was the grammar, in which we allow  two types of rules: {\emph non-terminal} and {\emph terminal} expansions. 
+The former rewrites a non-terminal symbol as a string of two or three non-terminals along with an alignment $\mathbf{a}$, specifying the corresponding ordering of the child trees in the source and target language. 
+Terminal expansion rewrite a non-terminal as a pair of terminal n-grams, where either but not both may be empty. 
+Given a grammar, each sentence is generated as follows, starting with the distinguished root non-terminal, $S$. 
+Rewrite each frontier non-terminal, $c$, using a rule chosen from our grammar expanding $c$. 
+Repeat until there are no remaining frontier non-terminals. 
+The sentences in both languages can then be read off the leaves, using the rules' alignments to find the right ordering. 
+
+\begin{figure}[t]
+  \centering
+  \subfigure{\includegraphics[scale=0.7]{intro_slides/PhraseExtraction1.pdf}}
+  \subfigure{\includegraphics[scale=0.7]{intro_slides/HieroExtraction2.pdf}}
+\caption{Extracting translation rules from aligned sentences. All the phrases obtained using the standard phrase extraction heuristics are depicted in the left figure, these are: $\langle$ Je, I $\rangle$, $\langle$ veux, want to $\rangle$, $\langle$ travailler, work $\rangle$, $\langle$ ne veux pas, do not want to $\rangle$, $\langle$ ne veux pas travailler, do not want to work $\rangle$, $\langle$ Je ne veux pas, I do not want to $\rangle$, $\langle$ Je ne veux pas travailler, I do not want to work $\rangle$. On the right is shown how a discontiguous SCFG rule is created by generalising a phrase embedded in another phrase, the extracted rule is: X $\rightarrow$ $\langle$ ne X$_1$ pas, do not X$_1$ $\rangle$.}
+\label{fig:intro_rule_extraction}
+\end{figure}
+
+The process for extracting SCFG rules is based on that used to extract translation phrases in phrase based translation systems. 
+The phrase based approach \cite{koehn03} uses heuristics to extract phrase translation pairs from a word-aligned corpus. 
+The phrase extraction heuristic is illustrated in Figure \ref{fig:intro_rule_extraction}. 
+This heuristic extracts all phrases whose words are either not aligned, or aligned with only other words in the same phrase. 
+The phrase translation probabilities are then calculated using a maximum likelihood estimation. 
+
+The Hiero \cite{chiang07hierarchical} SCFG extraction heuristic starts from a grammar consisting of the set of contiguous phrases, wherever a phrase is wholly embedded within another a new rule is add with the embedded phrase replace by the non-terminal X. 
+This process continues until all possible rules have been extracted, subject to the constraints that every rule must contain a terminal on the source side, a rule may only contain two non-terminals on its right side and that those non-terminals may not be adjacent.
+The left example in Figure \ref{fig:intro_rule_extraction} depicts this rule generalisation process. 
diff --git a/report/introduction.tex b/report/introduction.tex
index 12cc2705..41d1e81b 100644
--- a/report/introduction.tex
+++ b/report/introduction.tex
@@ -122,50 +122,3 @@ The algorithms we explored were Maximum Expected Bleu \cite{smith,li} and MIRA \
 Chapter \ref{chap:training} describes this work.
 
 The remainder of this introductory chapter provides a formal definition of SCFGs and describes the language pairs that we experimented with.
-
-\section{Synchronous context free grammar} \label{sec:scfg}
-{\em This section will be moved to the start of Chapter 2}
-
-%\subsubsection*{Synchronous context free grammar} \label{sec:scfg}
-\begin{figure}[t]
-\begin{center}
-\includegraphics[width=0.6\columnwidth]{example_derivation2.pdf}
-\end{center}
-\caption[Derivation]{An example SCFG derivation from a Chinese source sentence which yields the English sentence: {\em ``Brown arrived in Shanghai from Beijing late last night.''}. The non-terminal alignment $\mathbf{a}$ is specified by the variable subscripts.}
-\label{fig:intro_example_derivation}
-\end{figure}
-
-The translation models discussed explored in this workshop are based on synchronous grammars. 
-Here we provide a short definition of the formalism we've employed: synchronous context free grammar (SCFG).
-
-A synchronous context free grammar (SCFG, \cite{lewis68scfg}) generalizes context-free grammars to generate strings concurrently in two (or more) languages. A string pair is generated by applying a series of paired rewrite rules of the form, $X \rightarrow \langle \mathbf{e}, \mathbf{f}, \mathbf{a} \rangle$, where $X$ is a non-terminal, $\mathbf{e}$ and $\mathbf{f}$ are strings of terminals and non-terminals and $\mathbf{a}$ specifies a one-to-one alignment between non-terminals in $\mathbf{e}$ and $\mathbf{f}$.
-In the context of SMT, by assigning the source and target languages to the respective sides of a probabilistic SCFG it is possible to describe translation as the process of parsing the source sentence, which induces a parallel tree structure and translation in the target language \cite{chiang07hierarchical}.  
-Terminal are rewritten as pairs of strings of terminal symbols in the source and target languages.  Additionally, one side of a terminal expansion may be the special symbol $\epsilon$, which indicates a null alignment which permits arbitrary insertions and deletions.
-Figure \ref{fig:intro_example_derivation} is an example derivation for Chinese to English translation using an SCFG of the form that I propose to learn using non-parametric Bayesian models.
-
-The generative story is as follows. 
-In the beginning was the grammar, in which we allow  two types of rules: {\emph non-terminal} and {\emph terminal} expansions. 
-The former rewrites a non-terminal symbol as a string of two or three non-terminals along with an alignment $\mathbf{a}$, specifying the corresponding ordering of the child trees in the source and target language. 
-Terminal expansion rewrite a non-terminal as a pair of terminal n-grams, where either but not both may be empty. 
-Given a grammar, each sentence is generated as follows, starting with the distinguished root non-terminal, $S$. 
-Rewrite each frontier non-terminal, $c$, using a rule chosen from our grammar expanding $c$. 
-Repeat until there are no remaining frontier non-terminals. 
-The sentences in both languages can then be read off the leaves, using the rules' alignments to find the right ordering. 
-
-\begin{figure}[t]
-  \centering
-  \subfigure{\includegraphics[scale=0.7]{intro_slides/PhraseExtraction1.pdf}}
-  \subfigure{\includegraphics[scale=0.7]{intro_slides/HieroExtraction2.pdf}}
-\caption{Extracting translation rules from aligned sentences. All the phrases obtained using the standard phrase extraction heuristics are depicted in the left figure, these are: $\langle$ Je, I $\rangle$, $\langle$ veux, want to $\rangle$, $\langle$ travailler, work $\rangle$, $\langle$ ne veux pas, do not want to $\rangle$, $\langle$ ne veux pas travailler, do not want to work $\rangle$, $\langle$ Je ne veux pas, I do not want to $\rangle$, $\langle$ Je ne veux pas travailler, I do not want to work $\rangle$. On the right is shown how a discontiguous SCFG rule is created by generalising a phrase embedded in another phrase, the extracted rule is: X $\rightarrow$ $\langle$ ne X$_1$ pas, do not X$_1$ $\rangle$.}
-\label{fig:intro_rule_extraction}
-\end{figure}
-
-The process for extracting SCFG rules is based on that used to extract translation phrases in phrase based translation systems. 
-The phrase based approach \cite{koehn03} uses heuristics to extract phrase translation pairs from a word-aligned corpus. 
-The phrase extraction heuristic is illustrated in Figure \ref{fig:intro_rule_extraction}. 
-This heuristic extracts all phrases whose words are either not aligned, or aligned with only other words in the same phrase. 
-The phrase translation probabilities are then calculated using a maximum likelihood estimation. 
-
-The Hiero \cite{chiang07hierarchical} SCFG extraction heuristic starts from a grammar consisting of the set of contiguous phrases, wherever a phrase is wholly embedded within another a new rule is add with the embedded phrase replace by the non-terminal X. 
-This process continues until all possible rules have been extracted, subject to the constraints that every rule must contain a terminal on the source side, a rule may only contain two non-terminals on its right side and that those non-terminals may not be adjacent.
-The left example in Figure \ref{fig:intro_rule_extraction} depicts this rule generalisation process. 
diff --git a/report/report.tex b/report/report.tex
index 2b8e045f..71aa668f 100755
--- a/report/report.tex
+++ b/report/report.tex
@@ -93,6 +93,7 @@ We especially would like to thank Fred Jelinek for heading the Summer School eff
 
 
 \include{introduction}
+\include{SCFGs}
 \include{np_clustering}
 \include{pr-clustering/posterior}
 \include{training}