Neural Machine Translation with Phrasal Attention

Attention-based neural machine translation (NMT) employs an attention network to capture structural correspondences between the source and target language at the word level. Unfortunately, alignments between source and target equivalents are complicated, which makes word-level attention not adequate to model these relations (e.g., alignments between a source idiom and its target translation). In order to handle this issue, we propose a phrase-level attention mechanism to complement the word-level attention network in this paper. The proposed phrasal attention framework is simple yet effective, keeping the strength of phrase-based statistical machine translation (SMT) on the source side. Experiments on Chinese-to-English translation task demonstrate that the proposed method is able to statistically improve word-level attention-based NMT.