arXiv:2606.24825v1 Announce Type: new 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
The continuous drive for AI inclusivity and performance across diverse languages is leading to focused research efforts on under-resourced languages like Marathi, leveraging advancements in transformer models.
This development highlights the ongoing expansion of AI capabilities into non-English languages, critical for broader societal adoption and market penetration, especially in linguistically diverse regions.
The availability of specific datasets and BERT models for Marathi will improve the performance of NLP applications for this language, reducing resource disparity compared to major global languages.
- · Marathi-speaking populations
- · NLP researchers
- · Indian tech companies
- · Multilingual AI platforms
Improved machine translation, information extraction, and conversational AI for Marathi.
Increased digital content creation and consumption in Marathi, fostering local digital economies.
Potential for other under-resourced Indian languages to receive similar dedicated AI development, driving linguistic digital equity across the subcontinent.
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Read at arXiv cs.CL