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    <abstract>As the Information Retrieval (IR) field increasingly recognizes the importance of inclusivity, addressing the needs of low-resource languages remains a significant challenge. Transliteration between Urdu and its Romanized form, Roman Urdu, remains underexplored despite the widespread use of both scripts in South Asia. Prior work using RNNs on the Roman-Urdu-Parl dataset showed promising results but suffered from poor domain adaptability and limited evaluation. We propose a transformer-based approach using the m2m100 multilingual translation model, enhanced with masked language modeling (MLM) pretraining and fine-tuning on both Roman-Urdu-Parl and the domain diverse Dakshina dataset. To address previous evaluation flaws, we introduce rigorous dataset splits and assess performance using BLEU, character-level BLEU, and CHRF. Our model achieves strong transliteration performance, with Char-BLEU scores of 96.37 for Urdu→Roman-Urdu and 97.44 for Roman-Urdu→Urdu. These results outperform both RNN baselines and GPT-4o Mini and demonstrate the effectiveness of multilingual transfer learning for low-resource transliteration tasks.</abstract>
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%0 Conference Proceedings
%T Low-Resource Transliteration for Roman-Urdu and Urdu Using Transformer-Based Models
%A Butt, Umer
%A Varanasi, Stalin
%A Neumann, Günter
%Y Ojha, Atul Kr.
%Y Liu, Chao-hong
%Y Vylomova, Ekaterina
%Y Pirinen, Flammie
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Zhao, Xiaobing
%S Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, U.S.A.
%@ 979-8-89176-230-5
%F butt-etal-2025-low
%X As the Information Retrieval (IR) field increasingly recognizes the importance of inclusivity, addressing the needs of low-resource languages remains a significant challenge. Transliteration between Urdu and its Romanized form, Roman Urdu, remains underexplored despite the widespread use of both scripts in South Asia. Prior work using RNNs on the Roman-Urdu-Parl dataset showed promising results but suffered from poor domain adaptability and limited evaluation. We propose a transformer-based approach using the m2m100 multilingual translation model, enhanced with masked language modeling (MLM) pretraining and fine-tuning on both Roman-Urdu-Parl and the domain diverse Dakshina dataset. To address previous evaluation flaws, we introduce rigorous dataset splits and assess performance using BLEU, character-level BLEU, and CHRF. Our model achieves strong transliteration performance, with Char-BLEU scores of 96.37 for Urdu→Roman-Urdu and 97.44 for Roman-Urdu→Urdu. These results outperform both RNN baselines and GPT-4o Mini and demonstrate the effectiveness of multilingual transfer learning for low-resource transliteration tasks.
%R 10.18653/v1/2025.loresmt-1.13
%U https://aclanthology.org/2025.loresmt-1.13/
%U https://doi.org/10.18653/v1/2025.loresmt-1.13
%P 144-153

Markdown (Informal)

[Low-Resource Transliteration for Roman-Urdu and Urdu Using Transformer-Based Models](https://aclanthology.org/2025.loresmt-1.13/) (Butt et al., LoResMT 2025)

• Low-Resource Transliteration for Roman-Urdu and Urdu Using Transformer-Based Models (Butt et al., LoResMT 2025)

ACL

• Umer Butt, Stalin Varanasi, and Günter Neumann. 2025. Low-Resource Transliteration for Roman-Urdu and Urdu Using Transformer-Based Models. In Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025), pages 144–153, Albuquerque, New Mexico, U.S.A.. Association for Computational Linguistics.