\documentclass[landscape]{slides}
\usepackage[dvips]{color}
\usepackage{amssymb,amsmath}
\title{\textcolor{red}{Learning from Uncontrolled Corpora}}
\author{Katharina Probst}
\date{\today}

\begin{document}
\maketitle

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%%%%%%%%%%%%%%%%%%%%% NOTES %%%%%%%%%%%%%%%%%%%%%%%%%

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\begin{slide}
\begin{center}
\textcolor{red}{\textbf{Introduction}}
\end{center}
\begin{small}
Problem Definition:
\begin{itemize}
\item Given: parallel corpus, word alignments, a bilingual dictionary
\item Goal: learn syntactic rules from English into a TL (as before)
\end{itemize}
Notes: 
\begin{itemize}
\item The following slides represent what I tentatively plan to complete before the proposal, excluding the slide `Future work'.
\item The adjusted algorithms grew out of the circumstances of dealing with uncontrolled corpora.  They will equally be applied to a controlled corpus.
\end{itemize}
\end{small}
\end{slide}

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\begin{slide}
\begin{center}
\textcolor{red}{\textbf{Challenges when learning from uncontrolled corpora}}
\end{center}
\begin{itemize}
\item No feature-tagged TL dictionary
\item No compositional structure of training data
\item Long sentences
\item Possibly a very big transfer dictionary
\end{itemize}
\end{slide}

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\begin{slide}
\begin{center}
\textcolor{red}{\textbf{Tagging the TL dictionary}}
\end{center}
\begin{itemize}
\item Use English POS-tagger and alignments
\item Penn treebank tagset is rich enough to tag some features
\item Extract likely instances of plural nouns (/past tense verbs) in TL
\item Assume that feature is marked at the beginning and/or end of the word
\item Learn a classifier that can tag unseen instances
\end{itemize}
\end{slide}

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\begin{slide}
\begin{center}
\textcolor{red}{\textbf{Training data extraction}}
\end{center}
\begin{itemize}
\item Parse English sentence (Charniak parser)
\item Traverse parse from top down, extract training example for the chunk rooted at each node
\item For each training example, record 
\begin{small}
\begin{enumerate}
\item features from POS tags
\item corresponding TL chunk
\item partial parse
\item alignments%\footnote{Not the same as sentence-level alignments.}
\end{enumerate}
\end{small}
\end{itemize}
\end{slide}

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\begin{slide}
\begin{center}
\textcolor{red}{\textbf{Examples of extracted training instances}}
\end{center}
See file on avenue://usr0/kathrin/RuleLearning/\\VersionIndepFiles/IOFiles/InFiles/EFTrainingFromUnc.rulelearning.txt
\end{slide}

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\begin{slide}
\begin{center}
\textcolor{red}{\textbf{Seed Generation revisted}}
\end{center}
\begin{itemize}
\item Introduce all possible constraints
\item Lexical constraints only make sense if marked in the lexicon, e.g. if we introduce a constraint ((Y1 NUM) = (X2 NUM)), NUM must be marked in the TL lexicon
\item Introduce head-passing constraints in SL, see head rule table in avenue://usr0/kathrin/RuleLearning/HeadFinding/\\EnglishHeadTable.txt
\end{itemize}
\end{slide}

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\begin{slide}
\begin{center}
\textcolor{red}{\textbf{Seed Generation revisted (II)}}
\end{center}
\begin{itemize}
\item Examples of extracted training examples with English heads marked: avenue://usr0/kathrin/RuleLearning\\/VersionIndepFiles/IOFiles/InFiles\\/EFTrainingFromUnc.slheads.rulelearning.txt
\item Try to infer head in TL phrases, also pass up features up from head \textcolor{blue}{(may be future work)}
\item Also pass up features from non-heads, e.g. definiteness from determiners to NPs \textcolor{blue}{(future work)}
\end{itemize}
\end{slide}


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\begin{slide}
\begin{center}
\textcolor{red}{\textbf{Compositionality revisited}}
\end{center}
\begin{itemize}
\item Compositionality has been moved to be exclusively context-free $\rightarrow$ constraints are ignored
\item This allows the system to give the transfer engine only the lexical entries it needs for a phrase/sentence
\item This step now learns the context-free backbone of the transfer grammar, esp. important if few features are given
\item Examples of compositional (context-free) rules:
avenue://usr0/\\kathrin/RuleLearning/VersionIndepFiles/IOFiles/OutFiles/\\FinalGrammar.txt
\end{itemize}
\end{slide}

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\begin{slide}
\begin{center}
\textcolor{red}{\textbf{Seeded Version Space learning revisited}}
\end{center}
\begin{itemize}
\item With constraints introduced during Seed Generation, SVS starts with more specific rules
\item Ex. of a non-safe generalization: deletion of constraint
\item Ex. of a safe generalization: merging of two value constraints to an agreement constraint: ((Y3 NUM) = PL), ((Y4 NUM) = PL) $\rightarrow$ ((Y3 NUM) = (Y4 NUM))
\item Only allow for `safe' generalizations, non-safe generalizations are moved to the run-time system \textcolor{blue}{(future work)}
\end{itemize}
\end{slide}

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\begin{slide}
\begin{center}
\textcolor{red}{\textbf{Run-time system}}
\end{center}
\begin{itemize}
\item English parse is traversed top-down
\item At each node, translation is attempted
\item Empty translation ($\rightarrow$ skipping) allowed 
\item Partial translations are combined
\item Currently no way to distinguish between translations \textcolor{blue}{(future work)}
\end{itemize}
\end{slide}

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\begin{slide}
\begin{center}
\textcolor{blue}{\textbf{Future work}}
\end{center}
\begin{itemize}
\item TL dictionary feature tagging expanded
\item Run-time system revisited:
\begin{small}
\begin{itemize}
\item Incorporate rule scores, lexical translation probabilities
\item Incorporate probabilities of zero translations
\item Violation of constraints with penalty (transfer engine?)
\end{itemize}
\end{small}
\item Evaluation of algorithms
\end{itemize}
\end{slide}

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\begin{slide}
\begin{center}
\textcolor{red}{\textbf{Conclusion}}
\end{center}
\begin{itemize}
\item Learning from uncontrolled corpus as important extension to system
\item Automatically inferred TL feature values
\item Stronger burden on run-time system
\end{itemize}
\end{slide}

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\end{document}