Tree-to-Sequence Attentional Neural Machine Translation
Abstract
Most of the existing Neural Machine Translation (NMT) models focus on the conversion of sequential data and do not directly use syntactic information. We propose a novel end-to-end syntactic NMT model, extending a sequence-to-sequence model with the source-side phrase structure. Our model has an attention mechanism that enables the decoder to generate a translated word while softly aligning it with phrases as well as words of the source sentence. Experimental results on the WAT'15 English-to-Japanese dataset demonstrate that our proposed model considerably outperforms sequence-to-sequence attentional NMT models and compares favorably with the state-of-the-art tree-to-string SMT system.
Cite
@article{arxiv.1603.06075,
title = {Tree-to-Sequence Attentional Neural Machine Translation},
author = {Akiko Eriguchi and Kazuma Hashimoto and Yoshimasa Tsuruoka},
journal= {arXiv preprint arXiv:1603.06075},
year = {2016}
}
Comments
Accepted as a full paper at the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016)