Tree-to-Sequence Attentional Neural Machine Translation

by   Akiko Eriguchi, et al.
The University of Tokyo

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.


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Code Repositories


C++ implementation for Neural Network-based NLP, such as LSTM machine translation!

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C++ code of "Tree-to-Sequence Attentional Neural Machine Translation (tree2seq ANMT)"

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