Paper: Shift-Reduce Word Reordering for Machine Translation

ACL ID D13-1139
Title Shift-Reduce Word Reordering for Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

This paper presents a novel word reordering model that employs a shift-reduce parser for inversion transduction grammars. Our model uses rich syntax parsing features for word re- ordering and runs in linear time. We apply it to postordering of phrase-based machine trans- lation (PBMT) for Japanese-to-English patent tasks. Our experimental results show that our method achieves a significant improvement of +3.1 BLEU scores against 30.15 BLEU scores of the baseline PBMT system.