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author | philblunsom@gmail.com <philblunsom@gmail.com@ec762483-ff6d-05da-a07a-a48fb63a330f> | 2010-08-11 16:03:03 +0000 |
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committer | philblunsom@gmail.com <philblunsom@gmail.com@ec762483-ff6d-05da-a07a-a48fb63a330f> | 2010-08-11 16:03:03 +0000 |
commit | 549a2bf240bc968e414b668b938db475b232cf91 (patch) | |
tree | 981a8a354a5f4760ff7782e397d547ef27ac15c0 | |
parent | cfa303b746be4d3625c62fa0234ffda71bd7617d (diff) |
More intro...
git-svn-id: https://ws10smt.googlecode.com/svn/trunk@526 ec762483-ff6d-05da-a07a-a48fb63a330f
-rw-r--r-- | report/introduction.tex | 4 |
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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. |