Tree-to-Sequence Attentional Neural Machine Translation
This work addresses the challenge of improving translation accuracy in NMT by integrating syntactic structures, which is an incremental advancement over existing methods.
The paper tackled the problem of incorporating syntactic information into neural machine translation by proposing a tree-to-sequence attentional model that aligns translated words with source phrases and words. Experimental results on the WAT'15 English-to-Japanese dataset showed that the model outperformed sequence-to-sequence attentional NMT models and was competitive with state-of-the-art tree-to-string SMT systems.
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.