Neural Machine Translation with Source Dependency Representation
Kehai Chen, Rui Wang, Masao Utiyama, Lemao Liu, Akihiro Tamura, Eiichiro Sumita, Tiejun Zhao · 2017
Source dependency information has been successfully introduced into statistical machine translation.However, there are only a few preliminary attempts for Neural Machine Translation (NMT), such as concatenating representations of source word and its dependency label together.In this paper, we propose a novel attentional NMT with source dependency representation to improve translation performance of NMT, especially on long sentences.Empirical results on NIST Chinese-to-English translation task show that our method achieves 1.6 BLEU improvements on average over a strong NMT system.