NLIP_Lab-IITH Multilingual MT System for WAT24 MT Shared Task

• URL: http://arxiv.org/abs/2410.13443v1
• Date: Thu, 17 Oct 2024 11:18:23 GMT
• Title: NLIP_Lab-IITH Multilingual MT System for WAT24 MT Shared Task
• Authors: Maharaj Brahma, Pramit Sahoo, Maunendra Sankar Desarkar,
• Abstract summary: This paper describes NLIP Lab's multilingual machine translation system for the WAT24 shared task on multilingual Indic MT task. We explore pre-training for Indic languages using alignment agreement objectives. We fine-tuned language direction-specific multilingual translation models using small and high-quality seed data.
• Score: 9.476463361600826
• License:
• 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\footnote{Our code and models are available at \url{https://github.com/maharajbrahma/WAT2024-MultiIndicMT}} is competitive with IndicTransv1 (474M parameter model).

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