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        <title>TenTrans Large-Scale Multilingual Machine Translation System for WMT21</title>
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    <abstract>This paper describes TenTrans large-scale multilingual machine translation system for WMT 2021. We participate in the Small Track 2 in five South East Asian languages, thirty directions: Javanese, Indonesian, Malay, Tagalog, Tamil, English. We mainly utilized forward/back-translation, in-domain data selection, knowledge distillation, and gradual fine-tuning from the pre-trained model FLORES-101. We find that forward/back-translation significantly improves the translation results, data selection and gradual fine-tuning are particularly effective during adapting domain, while knowledge distillation brings slight performance improvement. Also, model averaging is used to further improve the translation performance based on these systems. Our final system achieves an average BLEU score of 28.89 across thirty directions on the test set.</abstract>
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        <url>https://aclanthology.org/2021.wmt-1.53</url>
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        <date>2021-nov</date>
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%0 Conference Proceedings
%T TenTrans Large-Scale Multilingual Machine Translation System for WMT21
%A Xie, Wanying
%A Hu, Bojie
%A Yang, Han
%A Yu, Dong
%A Ju, Qi
%S Proceedings of the Sixth Conference on Machine Translation
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Online
%F xie-etal-2021-tentrans
%X This paper describes TenTrans large-scale multilingual machine translation system for WMT 2021. We participate in the Small Track 2 in five South East Asian languages, thirty directions: Javanese, Indonesian, Malay, Tagalog, Tamil, English. We mainly utilized forward/back-translation, in-domain data selection, knowledge distillation, and gradual fine-tuning from the pre-trained model FLORES-101. We find that forward/back-translation significantly improves the translation results, data selection and gradual fine-tuning are particularly effective during adapting domain, while knowledge distillation brings slight performance improvement. Also, model averaging is used to further improve the translation performance based on these systems. Our final system achieves an average BLEU score of 28.89 across thirty directions on the test set.
%U https://aclanthology.org/2021.wmt-1.53
%P 439-445

Markdown (Informal)

[TenTrans Large-Scale Multilingual Machine Translation System for WMT21](https://aclanthology.org/2021.wmt-1.53) (Xie et al., WMT 2021)

• TenTrans Large-Scale Multilingual Machine Translation System for WMT21 (Xie et al., WMT 2021)

ACL

• Wanying Xie, Bojie Hu, Han Yang, Dong Yu, and Qi Ju. 2021. TenTrans Large-Scale Multilingual Machine Translation System for WMT21. In Proceedings of the Sixth Conference on Machine Translation, pages 439–445, Online. Association for Computational Linguistics.