Related papers: Yes-MT's Submission to the Low-Resource Indic Language Translation Shared Task in WMT 2024

Yes-MT's Submission to the Low-Resource Indic Language Translation Shared Task in WMT 2024

URL: http://arxiv.org/abs/2512.15226v1
Date: Wed, 17 Dec 2025 09:24:05 GMT
Title: Yes-MT's Submission to the Low-Resource Indic Language Translation Shared Task in WMT 2024
Abstract summary: This paper presents the systems submitted by the Yes-MT team for the Low-Resource Indic Language Translation Shared Task at WMT 2024.<n>The experiments explored various approaches, including fine-tuning pre-trained models.<n>Results were evaluated on the WMT23 Low-Resource Indic Language Translation Shared Task test data.
Score: 1.3177681589844814
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper presents the systems submitted by the Yes-MT team for the Low-Resource Indic Language Translation Shared Task at WMT 2024 (Pakray et al., 2024), focusing on translating between English and the Assamese, Mizo, Khasi, and Manipuri languages. The experiments explored various approaches, including fine-tuning pre-trained models like mT5 (Xue et al., 2020) and IndicBart (Dabre et al., 2021) in both multilingual and monolingual settings, LoRA (Hu et al., 2021) fine-tuning IndicTrans2 (Gala et al., 2023), zero-shot and few-shot prompting (Brown, 2020) with large language models (LLMs) like Llama 3 (Dubey et al., 2024) and Mixtral 8x7b (Jiang et al., 2024), LoRA supervised fine-tuning of Llama 3 (Mecklenburg et al., 2024), and training Transformer models (Vaswani, 2017) from scratch. The results were evaluated on the WMT23 Low-Resource Indic Language Translation Shared Task test data using SacreBLEU (Post, 2018) and CHRF (Popovic, 2015), highlighting the challenges of low-resource translation and the potential of LLMs for these tasks, particularly with fine-tuning.

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