PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Decoding by Dynamic Chunking for Statistical Machine Translation
Sirvan Yahyaei and Christof Monz
In: MT Summit XII, September 1-4, 2009, Ottawa, Canada.

Abstract

In this paper we present an extension of a phrase-based decoder that dynamically chunks, reorders, and applies phrase translations in tandem. A maximum entropy classifier is trained based on the word alignments to find the best positions to chunk the source sentence. No language specific or syntactic information is used to build the chunking classifier. Words inside the chunks are moved together to enable the decoder to make long-distance re-orderings to capture the word order differences between languages with different sentence structures. To keep the search space manageable, phrases inside the chunks are monotonically translated, thus by eliminating the unnecessary local re-orderings, it is possible to perform long-distance re-orderings beyond the common fixed distortion limit. Experiments on German to English translation are reported.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:6713
Deposited By:Christof Monz
Deposited On:08 March 2010