ArXiv

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

2026-06-23 · 17:10 UTC ·Hariom Ingle, Ronit Ghode, Ishwari Gondkar... ·1 min read
Authors
Hariom Ingle, Ronit Ghode, Ishwari Gondkar...
Categories
cs.CL, cs.LG
arXiv
https://arxiv.org/abs/2606.24825v1
PDF
https://arxiv.org/pdf/2606.24825v1

Brief

L3Cube-MahaPOS presents a 32,354-sentence gold-standard POS corpus for Marathi (UD-aligned 16-tag scheme) with Unicode normalisation, Devanagari-aware tokenisation, and noise filtering. The authors benchmarked HMM, CRF, BiLSTM (with CharCNN), MuRIL and MahaBERT-v2, reporting a top system at 88.67% token accuracy and 81.67% macro-F1; dataset, guidelines, and checkpoints are released.

Why it matters

L3Cube-MahaPOS provides 32,354 manually annotated Marathi news sentences using a 16-tag Universal Dependencies-aligned scheme; annotation included Unicode normalisation, Devanagari-aware tokenisation, and noise filtering.

Key details

  • Benchmarks cover six model families (HMM, CRF, BiLSTM, BiLSTM+CharCNN, MuRIL, MahaBERT-v2); the best system achieved 88.67% token-level accuracy and 81.67% macro-F1 across 15 evaluated tag classes.
  • Authors (Ingle, Ghode, Gondkar, Harad, Joshi) released the dataset, annotation guidelines, and trained checkpoints (arXiv:2606.24825v1, published 2026-06-23), addressing Marathi's under-resourced status despite ~83 million speakers and challenges like rich morphology, free word order, lack of capitalization, and code-mixing.
Source evidence

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.