mlsanityData to 14 Oct 2026

BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources

arXiv 2604.18423 ↗First posted 20 Apr 2026cs.CLcited by 1

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Abstract (arXiv)

India's linguistic landscape, spanning 22 scheduled languages and hundreds of marginalized dialects, has driven rapid growth in NLP datasets, benchmarks, and pretrained models. However, no dedicated survey consolidates resources developed specifically for Indian languages. Existing reviews either focus on a few high-resource languages or subsume Indian languages within broader multilingual settings, limiting coverage of low-resource and culturally diverse varieties. To address this gap, we present the first unified survey of Indian NLP resources, covering 200+ datasets, 50+ benchmarks, and 100+ models, tools, and systems across text, speech, multimodal, and culturally grounded tasks. We organize resources by linguistic phenomena, domains, and modalities; analyze trends in annotation, evaluation, and model design; and identify persistent challenges such as data sparsity, uneven language coverage, script diversity, and limited cultural and domain generalization. This survey offers a consolidated foundation for equitable, culturally grounded, and scalable NLP research in the Indian linguistic ecosystem.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 78 of its 503 references that are in the map. First posted on arXiv 20 Apr 2026.

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78 of 503 references.

References · 78

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38 more
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  1. 2026Towards the Roof of the World: NLP Progress in Tibetan Languagescited by 0