Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation
Jinchao Zhang, Mingxuan Wang, Qun Liu, Jie Zhou · 2017
This paper proposes three distortion models to explicitly incorporate the word reordering knowledge into attention-based Neural Machine Translation (NMT) for further improving translation performance.Our proposed models enable attention mechanism to attend to source words regarding both the semantic requirement and the word reordering penalty.Experiments on Chinese-English translation show that the approaches can improve word alignment quality and achieve significant translation improvements over a basic attention-based N-MT by large margins.Compared with previous works on identical corpora, our system achieves the state-of-the-art performance on translation quality.