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diff --git a/report/introduction.tex b/report/introduction.tex index adcd15b0..21e0e907 100644 --- a/report/introduction.tex +++ b/report/introduction.tex @@ -115,6 +115,10 @@ We were able to show that each of these techniques could lead to faster decoding Chapter \ref{chap:decoding} describes this work. \paragraph{3) Discriminative training labelled SCFG translation models} +The third stream of the workshop focussed on implementing discriminative training algorithms for the labelled SCFG translation models produced by our unsupervised grammar induction algorithms. +Though the existing MERT \cite{och02mert} training algorithm is directly applicable to these grammars, it doesn't allow us to optimise models with large numbers of fine grained features extracted from the labels we've induced. +In order to maximise the benefit from our induced grammars we explored and implemented discriminative training algorithms capable of handling thousands, rather than tens, of features. +The algorithms we explored were Maximum Expected Bleu \cite{smith,li} and MIRA \cite{chiang}. 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. |