Work

Tree-to-Sequence Attentional Neural Machine Translation

Year: 2016

Type: conference-paper

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 sequenceto-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 Englishto-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.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/p16-1078

Authors: Akiko Eriguchi, Kazuma Hashimoto, Yoshimasa Tsuruoka

Institution: The University of Tokyo

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Japan Society for the Promotion of Science, Core Research for Evolutional Science and Technology

Awards: 潜在的情報の推定モデルに基づく多言語処理の研究, G6718509927