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Cited by 265

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Tree-to-Sequence Attentional Neural Machine Translation

Open access
Eriguchi, Akiko, Hashimoto, Kazuma, Tsuruoka, Yoshimasa·2016·Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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.

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Metadata

Authors
Eriguchi, A., Hashimoto, K., & Tsuruoka, Y.
Venue
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Published
2016
Pages
823-833
OpenAlex
W2963661253
Cited by
265
References
4
FWCI
23.42
Type
conference paper
Language
en
Open access
hybrid

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