SamasÄmayik: A Parallel Dataset for Hindi-Sanskrit Machine Translation
This provides a new resource for low-resource Hindi-Sanskrit machine translation, though it is incremental as it focuses on dataset creation rather than methodological innovation.
The authors created Samasāmayik, a large-scale Hindi-Sanskrit parallel dataset of 92,196 sentences from contemporary sources, and showed that models trained on it achieve significant performance gains on in-domain test data while maintaining comparable performance on other test sets.
We release SamasÄmayik, a novel, meticulously curated, large-scale Hindi-Sanskrit corpus, comprising 92,196 parallel sentences. Unlike most data available in Sanskrit, which focuses on classical era text and poetry, this corpus aggregates data from diverse sources covering contemporary materials, including spoken tutorials, children's magazines, radio conversations, and instruction materials. We benchmark this new dataset by fine-tuning three complementary models - ByT5, NLLB and IndicTrans-v2, to demonstrate its utility. Our experiments demonstrate that models trained on the Samasamayik corpus achieve significant performance gains on in-domain test data, while achieving comparable performance on other widely used test sets, establishing a strong new performance baseline for contemporary Hindi-Sanskrit translation. Furthermore, a comparative analysis against existing corpora reveals minimal semantic and lexical overlap, confirming the novelty and non-redundancy of our dataset as a robust new resource for low-resource Indic language MT.