in neural machine translation, an attention model is used to identify the aligned source words for a target word target foresight word in order to select translation context, but it does not make use of any information of this target foresight word at all. previous work proposed an approach to improve the attention model by explicitly accessing this target foresight word and demonstrated the substantial gains in alignment task. however, this approach is useless in machine translation task on which the target foresight word is unavailable. in this paper, we propose a new attention model enhanced by the implicit information of target foresight word oriented to both alignment and translation tasks. empirical experiments on chineseto-english and japanese-to-english datasets show that the proposed attention model delivers significant improvements in terms of both alignment error rate and bleu.
REF presented a target foresight based attention which uses the POS tag as the partial information of a target foresight word to improve alignment and translation.
Target Foresight Based Attention for Neural Machine Translation
NAACL-HLT
Computer Science