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.
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.
A language-adaptive combination of generalist, specialist, and ensemble transformer models achieves 0.796 macro F1 and 0.826 accuracy on multilingual polarization detection across 22 languages.
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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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.