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.
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.
@cite_5
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While lexicalized reordering models have been widely used in phrase-based translation systems, they suffer from three drawbacks: context insensitivity, ambiguity, and sparsity. We propose a neural reordering model that conditions reordering probabilities on the words of both the current and previous phrase pairs. Including the words of previous phrase pairs significantly improves context sensitivity and reduces reordering ambiguity. To alleviate the data sparsity problem, we build one classifier for all phrase pairs, which are represented as continuous space vectors. Experiments on the NIST Chinese-English datasets show that our neural reordering model achieves significant improvements over state-of-the-art lexicalized reordering models.