Samasāmayik: A Parallel Dataset for Hindi-Sanskrit Machine Translation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| author | Karthika, N J Suryanarayanan, Keerthana Purohit, Jahanvi Ramakrishnan, Ganesh Singla, Jitin Gourishetty, Anil Kumar |
| author_facet | Karthika, N J Suryanarayanan, Keerthana Purohit, Jahanvi Ramakrishnan, Ganesh Singla, Jitin Gourishetty, Anil Kumar |
| contents | We release Samasāmayik, 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_24307 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Samasāmayik: A Parallel Dataset for Hindi-Sanskrit Machine Translation Karthika, N J Suryanarayanan, Keerthana Purohit, Jahanvi Ramakrishnan, Ganesh Singla, Jitin Gourishetty, Anil Kumar Computation and Language We release Samasāmayik, 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. |
| title | Samasāmayik: A Parallel Dataset for Hindi-Sanskrit Machine Translation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2603.24307 |