MTNLP-IIITH: Machine Translation for Low-Resource Indic Languages
P. M. Abhinav, Ketaki Shetye, Parameswari Krishnamurthy · 2024
Machine Translation for low-resource languages poses significant challenges, primarily due to the limited availability of data.The WMT24 Low-Resource Indic Neural Machine Translation task challenges us to employ innovative techniques to improve machine translation for low-resource Indian languages.Our proposed solution leverages advancements in neural machine translation, focusing on methodologies such as back-translation and fine-tuning.By fine-tuning pretrained models like mBART, we achieved significant progress in translating languages such as Manipuri and Khasi.The best score was achieved for the English-to-Khasi (en-kh) primary model, with the highest BLEU score of 0.0492, chrF score of 0.3316, and METEOR score of 0.2589 (on scale of 0 to 1) and comparable scores for other language pairs.