PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dynamic Distortion in a Discriminative Reordering Model for Statistical Machine Translation
Sirvan Yahyaei and Christof Monz
In: IWSLT 2010, 2-3 December 2010, Paris, France.

Abstract

Most phrase-based statistical machine translation systems use a so-called distortion limit to keep the size of the search space manageable. In addition, a distance-based distortion penalty is used as a feature to keep the decoder to translate monotonically unless there is sufficient support for a jump from other features, particularly the language models. To overcome the issue of setting the optimum distortion parameters in the phrase-based decoders and the fact that different sentences have different reordering requirements, a method to predict the necessary distortion limit for each sentence and each hypothesis expansion is proposed. A discriminative reordering model is built for that purpose and also integrated into the decoder as an extra feature. Many lexicalised and syntactic features of the source sentences are employed to predict the next reordering move of the decoder. The model scores each reordering before the sentence translation, so the optimum distortion limit can be estimated based on these score. Various experiments on Turkish to English and Arabic to English pairs are performed and substantial improvements are reported.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:8083
Deposited By:Christof Monz
Deposited On:17 March 2011