arXiv Machine Learning By Hariom Ingle, Ronit Ghode, Ishwari Gondkar, Jidnyasa Harad, Raviraj Joshi

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

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arXiv:2606. 24825v1 Announce Type: cross Abstract: Part-of-Speech (POS) tagging is a foundational NLP task underpinning machine translation, information extraction, and syntactic parsing.

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arXiv AI
Aug 20

NE-BERT: A Multilingual Language Model for Nine Northeast Indian Languages

NE‑BERT is a multilingual encoder trained on about 8.3 million sentences from nine Northeast Indian languages plus Hindi and English. Using weighted sampling and a custom SentencePiece tokenizer, it achieves significantly lower perplexity than IndicBERT‑V2, MuRIL, and mBERT, and improves tokenization fertility. The model also addresses vocabulary fragmentation in extremely low‑resource languages through aggressive upsampling, and its effectiveness is validated on part‑of‑speech tagging for three of the languages.

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arXiv Machine Learning
Jul 28

BHARATI: Morphology-Aware Tokenizers for Classical Indian Languages with Subword Fertility Analysis

arXiv:2607. 23319v1 Announce Type: cross Abstract: Standard subword tokenization algorithms such as Byte-Pair Encoding (BPE) and SentencePiece are trained predominantly on modern language corpora and produce inefficient segmentations when applied to classical Indian languages.

By Poornima Kumaresan, Pavithra Muruganantham, Lakshmi Rajendran, Santhosh Sivasubramani
arXiv Computation and Language
Sep 11

IndicTriMix: Developing Language Identification Datasets and Models for Tri-Language Code-Mixing

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