Chinese Syntactic Reordering for Statistical Machine Translation

Chao Wang, Michael Collins, Philipp Koehn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Syntactic reordering approaches are an effective method for handling word-order differences between source and target languages in statistical machine translation (SMT) systems. This paper introduces a reordering approach for translation from Chinese to English. We describe a set of syntactic reordering rules that exploit systematic differences between Chinese and English word order. The resulting system is used as a preprocessor for both training and test
sentences, transforming Chinese sentences to be much closer to English in terms of their word order. We evaluated the reordering approach within the MOSES phrase-based SMT system (Koehn et al., 2007). The reordering approach improved the BLEU score for the MOSES system from 28.52 to 30.86 on the NIST 2006 evaluation data. We also conducted a series of experiments to analyze
the accuracy and impact of different types of reordering rules.
Original languageEnglish
Title of host publicationEMNLP-CoNLL 2007, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, June 28-30, 2007, Prague, Czech Republic
PublisherAssociation for Computational Linguistics
Pages737-745
Number of pages9
Publication statusPublished - 2007

Fingerprint Dive into the research topics of 'Chinese Syntactic Reordering for Statistical Machine Translation'. Together they form a unique fingerprint.

Cite this