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arXiv · 1707.05436

Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder

Huadong Chen·Shujian Huang·David Chiang·Jiajun Chen
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Abstract

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.

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Huadong Chen, Shujian Huang, David Chiang, Jiajun Chen. 2017-07-18. Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder. https://arxiv.org/abs/1707.05436

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