Universal Neural Machine Translation for Extremely Low Resource Languages

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

Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologie (NAACL-HLT)., 2018

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.

[paper] [code]

Please cite as:

  title={Universal neural machine translation for extremely low resource languages},
  author={Gu, Jiatao and Hassan, Hany and Devlin, Jacob and Li, Victor OK},
  journal={arXiv preprint arXiv:1802.05368},