The attention mechanisim is appealing for neural machine translation, since it is able to dynam- ically encode a source sentence by generating a alignment between a target word and source words. Unfortunately, it has been proved to be worse than conventional alignment models in aligment accuracy. In this paper, we analyze and explain this issue from the point view of re- ordering, and propose a supervised attention which is learned with guidance from conventional alignment models. Experiments on two Chinese-to-English translation tasks show that the super- vised attention mechanism yields better alignments leading to substantial gains over the standard attention based NMT.
@cite_5: Statistical models for reordering source words have been used to enhance hierarchical phrase-based statistical machine translation. There are existing word-reordering models that learn reorderings for any two source words in a sentence or only for two contiguous words. This paper proposes a series of separate sub-models to learn reorderings for word pairs with different distances. Our experiments demonstrate that reordering sub-models for word pairs with distances less than a specific threshold are useful to improve translation quality. Compared with previous work, our method more effectively and efficiently exploits helpful word-reordering information; it improves a basic hierarchical phrase-based system by 2.4-3.1 BLEU points and keeps the average time of translating one sentence under 10 s.
It has always been standard practice to learn reordering models from alignments for conventional SMT either at the phrase level or word level. At the phrase level, proposed a lexicalized MSD model for phrasal reordering; proposed a feature-rich model to learn phrase reordering for BTG; and proposed a neural network method to learn a BTG reordering model. At the word level, surveyed many word reordering models learned from alignment models for SMT, and in particular there are some neural network based reordering models, such as @cite_5 . Our work is inspired by these works in spirit, and it can be considered to be a recurrent neural network based word-level reordering model. The main difference is that in our approach the reordering model and translation model are trained jointly rather than separately as theirs.