Guiding Neural Machine Translation with Bilingual Phrase Knowledge

Abstract

The past decade has witnessed the rapid development of Neural Machine Translation (NMT). However, NMT approaches tend to generate fluent but sometimes unfaithful translations of the source sentences. In response to this problem, we propose a new framework to incorporate the bilingual phrase knowledge into the encoder-decoder architecture, which allows the system to make full use of the phrase knowledge flexibly with no need to design complicated search algorithm. A significant difference to the existing work is that we obtain all the target phrases aligning to any part of the source sentence and learn representations for them before the decoding starts, which alleviates the hurt of invisibility of the future context in the standard autoregressive decoder, so that the generated target words can be decided more accurately with a global understanding. Extensive experiments on Japanese-Chinese translation task show that the proposed approach significantly outperforms multiple strong baselines in terms of BLEU scores, and verify the effectiveness of exploiting bilingual phrase knowledge for NMT.