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            <namePart type="given">Arnab</namePart>
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    <abstract>Grammar correction for Indian languages poses significant challenges due to complex morphology, non-standard spellings, and frequent script variations. In this work, we address grammar correction for English-mixed sentences in five Indic languages—Hindi, Bengali, Malayalam, Tamil, and Telugu—as part of the IndicGEC 2025 Bhasha Workshop. Our approach first applies word-level transliteration using IndicTrans (Bhat et al., 2014) to normalize Romanized and mixed-script tokens, followed by grammar correction using the mT5-small model (Xue et al., 2021). Although our experiments focus on these five languages, the methodology is generalizable to other Indian languages. Our implementation and code are publicly available at: https://github.com/Rucha-Ambaliya/bhasha-workshop</abstract>
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%0 Conference Proceedings
%T Niyamika at BHASHA Task 1: Word-Level Transliteration for English-Hindi Mixed Text in Grammar Correction Using MT5
%A Ambaliya, Rucha
%A Dugar, Mahika
%A Mishra, Pruthwik
%Y Bhattacharya, Arnab
%Y Goyal, Pawan
%Y Ghosh, Saptarshi
%Y Ghosh, Kripabandhu
%S Proceedings of the 1st Workshop on Benchmarks, Harmonization, Annotation, and Standardization for Human-Centric AI in Indian Languages (BHASHA 2025)
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-313-5
%F ambaliya-etal-2025-niyamika
%X Grammar correction for Indian languages poses significant challenges due to complex morphology, non-standard spellings, and frequent script variations. In this work, we address grammar correction for English-mixed sentences in five Indic languages—Hindi, Bengali, Malayalam, Tamil, and Telugu—as part of the IndicGEC 2025 Bhasha Workshop. Our approach first applies word-level transliteration using IndicTrans (Bhat et al., 2014) to normalize Romanized and mixed-script tokens, followed by grammar correction using the mT5-small model (Xue et al., 2021). Although our experiments focus on these five languages, the methodology is generalizable to other Indian languages. Our implementation and code are publicly available at: https://github.com/Rucha-Ambaliya/bhasha-workshop
%U https://aclanthology.org/2025.bhasha-1.13/
%P 135-141

Markdown (Informal)

[Niyamika at BHASHA Task 1: Word-Level Transliteration for English-Hindi Mixed Text in Grammar Correction Using MT5](https://aclanthology.org/2025.bhasha-1.13/) (Ambaliya et al., BHASHA 2025)

• Niyamika at BHASHA Task 1: Word-Level Transliteration for English-Hindi Mixed Text in Grammar Correction Using MT5 (Ambaliya et al., BHASHA 2025)

ACL

• Rucha Ambaliya, Mahika Dugar, and Pruthwik Mishra. 2025. Niyamika at BHASHA Task 1: Word-Level Transliteration for English-Hindi Mixed Text in Grammar Correction Using MT5. In Proceedings of the 1st Workshop on Benchmarks, Harmonization, Annotation, and Standardization for Human-Centric AI in Indian Languages (BHASHA 2025), pages 135–141, Mumbai, India. Association for Computational Linguistics.