Contrastive Learning for Many-to-many Multilingual Neural Machine Translation

• URL: http://arxiv.org/abs/2105.09501v1
• Date: Thu, 20 May 2021 03:59:45 GMT
• Title: Contrastive Learning for Many-to-many Multilingual Neural Machine Translation
• Authors: Xiao Pan, Mingxuan Wang, Liwei Wu, Lei Li
• Abstract summary: Existing multilingual machine translation approaches mainly focus on English-centric directions. We aim to build a many-to-many translation system with an emphasis on the quality of non-English language directions.
• Score: 16.59039088482523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing multilingual machine translation approaches mainly focus on English-centric directions, while the non-English directions still lag behind. In this work, we aim to build a many-to-many translation system with an emphasis on the quality of non-English language directions. Our intuition is based on the hypothesis that a universal cross-language representation leads to better multilingual translation performance. To this end, we propose \method, a training method to obtain a single unified multilingual translation model. mCOLT is empowered by two techniques: (i) a contrastive learning scheme to close the gap among representations of different languages, and (ii) data augmentation on both multiple parallel and monolingual data to further align token representations. For English-centric directions, mCOLT achieves competitive or even better performance than a strong pre-trained model mBART on tens of WMT benchmarks. For non-English directions, mCOLT achieves an improvement of average 10+ BLEU compared with the multilingual baseline.

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