mlsanityData to 14 Oct 2026

S=amayik: A Benchmark and Dataset for English-Sanskrit Translation

arXiv 2305.14004 ↗First posted 23 May 2023cs.CLcited by 3

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Abstract (arXiv)

We release S=amayik, a dataset of around 53,000 parallel English-Sanskrit sentences, written in contemporary prose. Sanskrit is a classical language still in sustenance and has a rich documented heritage. However, due to the limited availability of digitized content, it still remains a low-resource language. Existing Sanskrit corpora, whether monolingual or bilingual, have predominantly focused on poetry and offer limited coverage of contemporary written materials. S=amayik is curated from a diverse range of domains, including language instruction material, textual teaching pedagogy, and online tutorials, among others. It stands out as a unique resource that specifically caters to the contemporary usage of Sanskrit, with a primary emphasis on prose writing. Translation models trained on our dataset demonstrate statistically significant improvements when translating out-of-domain contemporary corpora, outperforming models trained on older classical-era poetry datasets. Finally, we also release benchmark models by adapting four multilingual pre-trained models, three of them have not been previously exposed to Sanskrit for translating between English and Sanskrit while one of them is multi-lingual pre-trained translation model including English and Sanskrit. The dataset and source code is present at https://github.com/ayushbits/saamayik.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 8 of its 23 references that are in the map. First posted on arXiv 23 May 2023.

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8 of 23 references.

References · 8

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  2. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  3. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
  4. 2021ByT5: Towards a token-free future with pre-trained byte-to-byte modelscited by 509
  5. 2021IndicBART: A Pre-trained Model for Indic Natural Language Generationcited by 39
  6. 2021Itihasa: A large-scale corpus for Sanskrit to English translationcited by 13
  7. 2022A Benchmark and Dataset for Post-OCR text correction in Sanskritcited by 10
  8. 2018Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskritcited by 8

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Citing papers per year: 2025: 1; 2026: 2.

3 papers.