L3Cube-MahaPOS: A Marathi Part-of-Speech Tagging Dataset and BERT Models

• URL: http://arxiv.org/abs/2606.24825v1
• Date: Tue, 23 Jun 2026 17:10:46 GMT
• Title: L3Cube-MahaPOS: A Marathi Part-of-Speech Tagging Dataset and BERT Models
• Abstract summary: Part-of-Speech (POS) tagging is a foundational NLP task underpinning machine translation, information extraction, and syntactic parsing.<n>Marathi is spoken by over 83 million people and ranks among the top twenty most spoken languages worldwide.<n>We benchmark the dataset across six model families spanning HMM, CRF, BiLSTM, BiLSTM+CharCNN, MuRIL, and the Marathi-specific transformer MahaBERT-v2.<n>The best system achieves 88.67% token-level accuracy and a macro-F1 of 81.67% over 15 evaluated tag classes.
• Score: 2.584263027095689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Part-of-Speech (POS) tagging is a foundational NLP task underpinning machine translation, information extraction, and syntactic parsing. Despite Marathi being spoken by over 83 million people and ranking among the top twenty most spoken languages worldwide, it remains severely under-resourced in annotated corpora and standardised evaluation benchmarks. Marathi presents unique challenges for computational modelling owing to its rich morphology, relatively free word order, lack of capitalisation conventions, and pervasive code-mixing with Hindi and English. We introduce L3Cube-MahaPOS, a gold-standard POS tagging dataset for Marathi comprising 32,354 manually annotated sentences drawn from news text. Annotation was performed entirely manually by a team of Marathi-proficient annotators following a 16-tag Universal Dependencies-aligned scheme. A structured preprocessing pipeline covering Unicode normalisation, Devanagari-aware tokenisation, and noise filtering ensures label consistency across all splits. We benchmark the dataset across six model families spanning HMM, CRF, BiLSTM, BiLSTM+CharCNN, MuRIL, and the Marathi-specific transformer MahaBERT-v2. The best system achieves 88.67\% token-level accuracy and a macro-F1 of 81.67% over 15 evaluated tag classes. We release the dataset, annotation guidelines, and trained model checkpoints to foster further research in Marathi NLP.

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