A unified survey that consolidates Indian NLP resources by task, language, domain, and modality while identifying gaps in coverage and generalization.
Muril: Multilingual representations for indian languages
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
representative citing papers
Human rationales in supervision for Telugu sentiment analysis improve model alignment with human reasoning and often produce gains in predictive performance.
KS-PRET-5M is a newly released 5.09 million word Kashmiri pretraining dataset containing 12.13 million subword tokens after MuRIL tokenization, made available as a continuous text stream under CC BY 4.0.
Per-language architecture selection among generalists, specialists, and ensembles achieves 0.796 macro F1 across 22 languages in SemEval-2026 Task 9.
Constructs gender-perturbed Bangla classification benchmarks and proposes RandSymKL debiasing that reduces extrinsic gender bias in pretrained models.
AgriGov is a new structured trilingual dataset of ~8000 sentence pairs from 50 Indian farmer schemes, created via scraping, MT pipeline, and corpus augmentation for NLP tasks.
Empirical study finds consistent positive correlation between inter-district geographic distance and ASR word error rate when models are finetuned on single-district Indic speech data.
A survey that taxonomizes motivations for transliteration in cross-lingual NLP, reviews incorporation approaches and their evolution, analyzes trade-offs in settings like code-mixing and language families, and offers implementation recommendations.
citing papers explorer
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BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources
A unified survey that consolidates Indian NLP resources by task, language, domain, and modality while identifying gaps in coverage and generalization.
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Human-Centered Supervision for Sentiment Analysis in Telugu: A Systematic Inquiry Beyond Accuracy
Human rationales in supervision for Telugu sentiment analysis improve model alignment with human reasoning and often produce gains in predictive performance.
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ks-pret-5m: a 5 million word, 12 million token kashmiri pretraining dataset
KS-PRET-5M is a newly released 5.09 million word Kashmiri pretraining dataset containing 12.13 million subword tokens after MuRIL tokenization, made available as a continuous text stream under CC BY 4.0.
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MKJ at SemEval-2026 Task 9: A Comparative Study of Generalist, Specialist, and Ensemble Strategies for Multilingual Polarization
Per-language architecture selection among generalists, specialists, and ensembles achieves 0.796 macro F1 across 22 languages in SemEval-2026 Task 9.
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Mitigating Extrinsic Gender Bias for Bangla Classification Tasks
Constructs gender-perturbed Bangla classification benchmarks and proposes RandSymKL debiasing that reduces extrinsic gender bias in pretrained models.
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AgriGov: A Structured Multilingual Dataset Curation for Indian Government Schemes for Farmers
AgriGov is a new structured trilingual dataset of ~8000 sentence pairs from 50 Indian farmer schemes, created via scraping, MT pipeline, and corpus augmentation for NLP tasks.
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A study on the impact of region specific data on the performance of Indic ASR
Empirical study finds consistent positive correlation between inter-district geographic distance and ASR word error rate when models are finetuned on single-district Indic speech data.
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Scripts Through Time: A Survey of the Evolving Role of Transliteration in NLP
A survey that taxonomizes motivations for transliteration in cross-lingual NLP, reviews incorporation approaches and their evolution, analyzes trade-offs in settings like code-mixing and language families, and offers implementation recommendations.