Shift-Reduce Word Reordering for Machine Translation
Katsuhiko Hayashi, Katsuhito Sudoh, Hajime Tsukada, Jun Suzuki, Masaaki Nagata · 2013
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 reordering and runs in linear time.We apply it to postordering of phrase-based machine translation (PBMT) for Japanese-to-English patent tasks.Our experimental results show that our method achieves a significant improvement of +3.1 BLEU scores against 30.15BLEU scores of the baseline PBMT system.