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    <abstract>This paper describes the submission to the WAT 2021 Indic Language Multilingual Task by Samsung R&amp;D Institute Poland. The task covered translation between 10 Indic Languages (Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil and Telugu) and English. We combined a variety of techniques: transliteration, filtering, backtranslation, domain adaptation, knowledge-distillation and finally ensembling of NMT models. We applied an effective approach to low-resource training that consist of pretraining on backtranslations and tuning on parallel corpora. We experimented with two different domain-adaptation techniques which significantly improved translation quality when applied to monolingual corpora. We researched and applied a novel approach for finding the best hyperparameters for ensembling a number of translation models. All techniques combined gave significant improvement - up to +8 BLEU over baseline results. The quality of the models has been confirmed by the human evaluation where SRPOL models scored best for all 5 manually evaluated languages.</abstract>
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    <identifier type="doi">10.18653/v1/2021.wat-1.27</identifier>
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%0 Conference Proceedings
%T Samsung R&D Institute Poland submission to WAT 2021 Indic Language Multilingual Task
%A Dobrowolski, Adam
%A Szymański, Marcin
%A Chochowski, Marcin
%A Przybysz, Paweł
%S Proceedings of the 8th Workshop on Asian Translation (WAT2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F dobrowolski-etal-2021-samsung
%X This paper describes the submission to the WAT 2021 Indic Language Multilingual Task by Samsung R&D Institute Poland. The task covered translation between 10 Indic Languages (Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil and Telugu) and English. We combined a variety of techniques: transliteration, filtering, backtranslation, domain adaptation, knowledge-distillation and finally ensembling of NMT models. We applied an effective approach to low-resource training that consist of pretraining on backtranslations and tuning on parallel corpora. We experimented with two different domain-adaptation techniques which significantly improved translation quality when applied to monolingual corpora. We researched and applied a novel approach for finding the best hyperparameters for ensembling a number of translation models. All techniques combined gave significant improvement - up to +8 BLEU over baseline results. The quality of the models has been confirmed by the human evaluation where SRPOL models scored best for all 5 manually evaluated languages.
%R 10.18653/v1/2021.wat-1.27
%U https://aclanthology.org/2021.wat-1.27
%U https://doi.org/10.18653/v1/2021.wat-1.27
%P 224-232

Markdown (Informal)

[Samsung R&D Institute Poland submission to WAT 2021 Indic Language Multilingual Task](https://aclanthology.org/2021.wat-1.27) (Dobrowolski et al., WAT 2021)

• Samsung R&D Institute Poland submission to WAT 2021 Indic Language Multilingual Task (Dobrowolski et al., WAT 2021)

ACL

• Adam Dobrowolski, Marcin Szymański, Marcin Chochowski, and Paweł Przybysz. 2021. Samsung R&D Institute Poland submission to WAT 2021 Indic Language Multilingual Task. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 224–232, Online. Association for Computational Linguistics.