Tree to Sequence Attentional Neural Machine Translation
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.
ACL