Evaluating IndicTrans2 and ByT5 for English-Santali Machine Translation Using the Ol Chiki Script

In this study, we examine and evaluate two multilingual NMT models, IndicTrans2 and ByT5, for English-Santali bidirectional translation using the Ol Chiki script. The models are trained on the MMLoSo Shared Task dataset, supplemented with public English-Santali resources, and evaluated on the AI4Bharat IN22 and Flores test sets, specifically IN22-Gen and Flores200-dev. IndicTrans2 finetune strongly outperforms ByT5 across both directions. On IN22-Gen, it achieves 26.8 BLEU and 53.9 chrF++ for Santali→English and 7.3 BLEU and 40.3 chrF++ for English→Santali, compared to ByT5's 5.6 BLEU and 30.2 chrF++ for Santali→English and 2.9 BLEU and 32.6 chrF++ for English→Santali. On the Flores test set, IndicTrans2 finetune achieves 22 BLEU, 49.2 chrF++, and 4.7 BLEU, 32.7 chrF++. Again, it surpasses ByT5. While ByT5's bytelevel modelling is script-agnostic, it struggles with Santali morphology. IndicTrans2 benefits from multilingual pre-training and script unification.