Low-Resource Transliteration for Roman-Urdu and Urdu Using Transformer-Based Models

• URL: http://arxiv.org/abs/2503.21530v2
• Date: Fri, 04 Apr 2025 09:55:38 GMT
• Title: Low-Resource Transliteration for Roman-Urdu and Urdu Using Transformer-Based Models
• Authors: Umer Butt, Stalin Veranasi, Günter Neumann,
• Abstract summary: Transliteration between Urdu and its Romanized form, Roman Urdu, remains underexplored.<n>We propose a transformer-based approach using the m2m100 multilingual translation model.<n>Our model achieves strong transliteration performance, with Char-BLEU scores of 96.37 for Urdu->Roman-Urdu and 97.44 for Roman-Urdu->Urdu.
• Score: 0.6554326244334868
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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