The SETU-ADAPT Submissions to the WMT24 Low-Resource Indic Language Translation Task

AbstractThis paper presents the SETU-ADAPT’s submissions to the WMT 2024 Low-Resource Indic Language Translation task. We participated in the unconstrained segment of the task, focusing on the Assamese-to-English and English-to-Assamese language pairs. Our approach involves leveraging Large Language Models (LLMs) as the baseline systems for all our MT tasks. Furthermore, we applied various strategies to improve the baseline systems. In our first approach, we fine-tuned LLMs using all the data provided by the task organisers. Our second approach explores in-context learning by focusing on few-shot prompting. In our final approach we explore an efficient data extraction technique based on a fuzzy match-based similarity measure for fine-tuning. We evaluated our systems using BLEU, chrF, WER, and COMET. The experimental results showed that our strategies can effectively improve the quality of translations in low-resource scenarios.