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Multi-way, multilingual neural machine translation
Date
2017-09-01
Author
Firat, Orhan
Cho, Kyunghyun
Sankaran, Baskaran
Yarman Vural, Fatoş Tunay
Bengio, Yoshua
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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We propose multi-way, multilingual neural machine translation. The proposed approach enables a single neural translation model to translate between multiple languages, with a number of parameters that grows only linearly with the number of languages. This is made possible by having a single attention mechanism that is shared across all language pairs. We train the proposed multi-way, multilingual model on ten language pairs from WMT'15 simultaneously and observe clear performance improvements over models trained on only one language pair. We empirically evaluate the proposed model on low-resource language translation tasks. In particular, we observe that the proposed multilingual model outperforms strong conventional statistical machine translation systems on Turkish-English and Uzbek-English by incorporating the resources of other language pairs. (C) 2016 Elsevier Ltd. All rights reserved
Subject Keywords
Low resource translation
,
Multi-lingual
,
Neural machine translation
URI
https://hdl.handle.net/11511/42997
Journal
COMPUTER SPEECH AND LANGUAGE
DOI
https://doi.org/10.1016/j.csl.2016.10.006
Collections
Department of Computer Engineering, Article
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O. Firat, K. Cho, B. Sankaran, F. T. Yarman Vural, and Y. Bengio, “Multi-way, multilingual neural machine translation,”
COMPUTER SPEECH AND LANGUAGE
, pp. 236–252, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42997.