Generalized Reordering Rules for Improved SMT

Fei Huang and Cezar Pendus

The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013

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Abstract

We present a simple yet effective approach to syntactic reordering for Statistical Machine Translation (SMT). Instead of solely relying on the top-1 best-matching rule for source sentence preordering, we generalize fully lexicalized rules into partially lexicalized and unlexicalized rules to broaden the rule coverage. Furthermore, , we consider multiple permutations of all the matching rules, and select the final reordering path based on the weighed sum of reordering probabilities of these rules. Our experiments in English-Chinese and English-Japanese translations demonstrate the effectiveness of the proposed approach: we observe consistent and significant improvement in translation quality across multiple test sets in both language pairs judged by both humans and automatic metric.

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