Paper: Generalized Reordering Rules for Improved SMT

ACL ID P13-2069
Title Generalized Reordering Rules for Improved SMT
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013

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 auto...