Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder
Huadong Chen, Shujian Huang, David Chiang, Jiajun Chen · 2017
Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information.In this paper, we improve this model by explicitly incorporating source-side syntactic trees.More specifically, we propose (1) a bidirectional tree encoder which learns both sequential and tree structured representations; (2) a tree-coverage model that lets the attention depend on the source-side syntax.Experiments on Chinese-English translation demonstrate that our proposed models outperform the sequential attentional model as well as a stronger baseline with a bottom-up tree encoder and word coverage.1