Universal Neural Machine Translation for Extremely Low Resource Languages

Jiatao Gu, Hany Hassan, Jacob Devlin, Victor O.K. Li


Abstract
In this paper, we propose a new universal machine translation approach focusing on languages with a limited amount of parallel data. Our proposed approach utilizes a transfer-learning approach to share lexical and sentence level representations across multiple source languages into one target language. The lexical part is shared through a Universal Lexical Representation to support multi-lingual word-level sharing. The sentence-level sharing is represented by a model of experts from all source languages that share the source encoders with all other languages. This enables the low-resource language to utilize the lexical and sentence representations of the higher resource languages. Our approach is able to achieve 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences, compared to the 18 BLEU of strong baseline system which uses multi-lingual training and back-translation. Furthermore, we show that the proposed approach can achieve almost 20 BLEU on the same dataset through fine-tuning a pre-trained multi-lingual system in a zero-shot setting.
Anthology ID:
N18-1032
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
344–354
Language:
URL:
https://aclanthology.org/N18-1032
DOI:
10.18653/v1/N18-1032
Bibkey:
Cite (ACL):
Jiatao Gu, Hany Hassan, Jacob Devlin, and Victor O.K. Li. 2018. Universal Neural Machine Translation for Extremely Low Resource Languages. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 344–354, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Universal Neural Machine Translation for Extremely Low Resource Languages (Gu et al., NAACL 2018)
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