Word Position Aware Translation Memory for Neural Machine Translation

The approach based on translation pieces is appealing for neural machine translation with a translation memory (TM), owing to its efficiency in both computation and memory consumption. Unfortunately, it is incapable of capturing sufficient contextual translation leading to a limited translation performance. This paper thereby proposes a simple yet effective approach to address this issue. Its key idea is to employ the word position information from a TM as additional rewards to guide the decoding of neural machine translation (NMT). Experiments on seven tasks show that the proposed approach yields consistent gains particularly for those source sentences whose TM is very similar to themselves, while maintaining similar efficiency to the counterpart of translation pieces.