  Non-autoregressive translation (NAT) significantly accelerates the inference
process by predicting the entire target sequence. However, due to the lack of
target dependency modelling in the decoder, the conditional generation process
heavily depends on the cross-attention. In this paper, we reveal a localness
perception problem in NAT cross-attention, for which it is difficult to
adequately capture source context. To alleviate this problem, we propose to
enhance signals of neighbour source tokens into conventional cross-attention.
Experimental results on several representative datasets show that our approach
can consistently improve translation quality over strong NAT baselines.
Extensive analyses demonstrate that the enhanced cross-attention achieves
better exploitation of source contexts by leveraging both local and global
information.
