Tree-to-sequence Attentional Neural Machine Translation
Akiko Eriguchi, Kazuma Hashimoto, Yoshimasa Tsuruoka . Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2016 – 256 citations
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.