S amsung R & D Institute P oland submission to WAT 2021 Indic Language Multilingual Task (2021)

Abstract: 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.

## introduction samsung r&d poland team researched effective techniques that worked especially well for lowresource languages: transliteration, iterative backtranslation followed by tuning on parallel corpora. we successfully applied these techniques during the wat2021 competition @xcite . especially for the competition we also applied custom domain-adaptation techniques which substantially improved the final results. most of the applied techniques and ideas are commonly used for works on indian languages machine translation @xcite . this document is structured as follows. in section 2 we describe the sources and techniques of corpora preparation used for the training. in sections 3 and 4 we describe the model architecture and techniques used in training, tuning and ensembling and finally section 5 presents the results we gained on every stage of the training. all trainings were performed on transformer models. we used standard marian nmt foot_0 v.1.9 framework. ## nmt system overview all of our systems are trained with the marian nmt foot_2 (junczys-dowmunt et al., 2018) framework. ## final results the detailed results of each stage of the best branch of trainings are gathered in tables 5 and 6 . the ensemble values are the submission evaluation results provided by the organizers. tables 7 and 8 contain the results of the models submitted by srpol compared with best results of competitors. the tables present values provided by wat2021 organizers, calculated by 3 different metrics: bleu @xcite , ribes @xcite , amfm @xcite figure 1 shows the results of the human evaluation. the figure presents the values provided by wat2021 organizers showing significant advance over the competitors. especially amount of bad translations (scored 1-2) has been significantly reduced. ## conclusions we presented an effective approach to low-resource training consisting of pretraining on backtranslations and tuning on parallel corpora. we successfully applied domain-adaptation techniques which significantly improved translation quality measured by bleu. we presented an effective approach for finding best hyperparameters for the ensembling number of single translation models. we applied transliteration, but the final results did not confirm that this approach is effective, at least for that particular task. we tried several filtering techniques for parallel corpora but the results showed no improvement. this may be a confirmation that the parallel corpora provided by the competition organizers are of high quality which is hard to improve. probably for the same reason domain-adaptation on parallel corpora didn't improve the results. however domain-adaptation worked surprisingly well for monolingual corpora.