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        <title>NLIP-Lab-IITH Multilingual MT System for WAT24 MT Shared Task</title>
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        <namePart type="given">Maharaj</namePart>
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            <title>Proceedings of the Ninth Conference on Machine Translation</title>
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    <abstract>This paper describes NLIP Lab’s multilingual machine translation system for the WAT24 shared task on multilingual Indic MT task for 22 scheduled languages belonging to 4 language families. We explore pre-training for Indic languages using alignment agreement objectives. We utilize bi-lingual dictionaries to substitute words from source sentences. Furthermore, we fine-tuned language direction-specific multilingual translation models using small and high-quality seed data. Our primary submission is a 243M parameters multilingual translation model covering 22 Indic languages. In the IN22-Gen benchmark, we achieved an average chrF++ score of 46.80 and 18.19 BLEU score for the En-Indic direction. In the Indic-En direction, we achieved an average chrF++ score of 56.34 and 30.82 BLEU score. In the In22-Conv benchmark, we achieved an average chrF++ score of 43.43 and BLEU score of 16.58 in the En-Indic direction, and in the Indic-En direction, we achieved an average of 52.44 and 29.77 for chrF++ and BLEU respectively. Our model is competitive with IndicTransv1 (474M parameter model).</abstract>
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%0 Conference Proceedings
%T NLIP-Lab-IITH Multilingual MT System for WAT24 MT Shared Task
%A Brahma, Maharaj
%A Sahoo, Pramit
%A Desarkar, Maunendra Sankar
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F brahma-etal-2024-nlip
%X This paper describes NLIP Lab’s multilingual machine translation system for the WAT24 shared task on multilingual Indic MT task for 22 scheduled languages belonging to 4 language families. We explore pre-training for Indic languages using alignment agreement objectives. We utilize bi-lingual dictionaries to substitute words from source sentences. Furthermore, we fine-tuned language direction-specific multilingual translation models using small and high-quality seed data. Our primary submission is a 243M parameters multilingual translation model covering 22 Indic languages. In the IN22-Gen benchmark, we achieved an average chrF++ score of 46.80 and 18.19 BLEU score for the En-Indic direction. In the Indic-En direction, we achieved an average chrF++ score of 56.34 and 30.82 BLEU score. In the In22-Conv benchmark, we achieved an average chrF++ score of 43.43 and BLEU score of 16.58 in the En-Indic direction, and in the Indic-En direction, we achieved an average of 52.44 and 29.77 for chrF++ and BLEU respectively. Our model is competitive with IndicTransv1 (474M parameter model).
%U https://aclanthology.org/2024.wmt-1.74
%P 804-809

Markdown (Informal)

[NLIP-Lab-IITH Multilingual MT System for WAT24 MT Shared Task](https://aclanthology.org/2024.wmt-1.74) (Brahma et al., WMT 2024)

• NLIP-Lab-IITH Multilingual MT System for WAT24 MT Shared Task (Brahma et al., WMT 2024)

ACL

• Maharaj Brahma, Pramit Sahoo, and Maunendra Sankar Desarkar. 2024. NLIP-Lab-IITH Multilingual MT System for WAT24 MT Shared Task. In Proceedings of the Ninth Conference on Machine Translation, pages 804–809, Miami, Florida, USA. Association for Computational Linguistics.