Contrastive Learning for Many-to-many Multilingual Neural Machine Translation
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Abstract (arXiv)
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 mRASP2, a training method to obtain a single unified multilingual translation model. mRASP2 is empowered by two techniques: a) a contrastive learning scheme to close the gap among representations of different languages, and b) data augmentation on both multiple parallel and monolingual data to further align token representations. For English-centric directions, mRASP2 outperforms existing best unified model and achieves competitive or even better performance than the pre-trained and fine-tuned model mBART on tens of WMT's translation directions. For non-English directions, mRASP2 achieves an improvement of average 10+ BLEU compared with the multilingual Transformer baseline. Code, data and trained models are available at https://github.com/PANXiao1994/mRASP2.
Key facts. Cited by 108 papers in the map (data through 7 Oct 2026). Builds on 40 of its 48 references that are in the map. First posted on arXiv 20 May 2021.
Builds on
40 of 48 references.
References · 40
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- 2019Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlationscited by 61
- 2020Cross-lingual Retrieval for Iterative Self-Supervised Trainingcited by 54
- 2020Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translationcited by 51
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- 2019Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translationcited by 23
- 2017A Teacher-Student Framework for Zero-Resource Neural Machine Translationcited by 22
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- 2017Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languagescited by 10
- 2018Zero-Resource Neural Machine Translation with Multi-Agent Communication Gamecited by 8
- 2019Multilingual Multi-Domain Adaptation Approaches for Neural Machine Translationcited by 8
- 2020Training Multilingual Machine Translation by Alternately Freezing Language-Specific Encoders-Decoderscited by 8
- 2017Effective Strategies in Zero-Shot Neural Machine Translationcited by 6
- 2017Improving a Multi-Source Neural Machine Translation Model with Corpus Extension for Low-Resource Languagescited by 4
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