Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder
This work addresses translation quality for language pairs with complex syntax, but it is incremental as it builds on existing encoder-decoder frameworks.
The paper tackled the problem of neural machine translation lacking syntactic information by incorporating source-side syntactic trees, resulting in improved performance over sequential and tree-based baselines on Chinese-English translation.
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.