Transformer-Based Low-Resource Language Translation: A Study on Standard Bengali to Sylheti

• URL: http://arxiv.org/abs/2510.18898v1
• Date: Mon, 20 Oct 2025 16:29:24 GMT
• Title: Transformer-Based Low-Resource Language Translation: A Study on Standard Bengali to Sylheti
• Authors: Mangsura Kabir Oni, Tabia Tanzin Prama,
• Abstract summary: We investigate Bengali-to-Sylheti translation by fine-tuning multilingual Transformer models.<n> Experimental results demonstrate that fine-tuned models significantly outperform large language models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine Translation (MT) has advanced from rule-based and statistical methods to neural approaches based on the Transformer architecture. While these methods have achieved impressive results for high-resource languages, low-resource varieties such as Sylheti remain underexplored. In this work, we investigate Bengali-to-Sylheti translation by fine-tuning multilingual Transformer models and comparing them with zero-shot large language models (LLMs). Experimental results demonstrate that fine-tuned models significantly outperform LLMs, with mBART-50 achieving the highest translation adequacy and MarianMT showing the strongest character-level fidelity. These findings highlight the importance of task-specific adaptation for underrepresented languages and contribute to ongoing efforts toward inclusive language technologies.

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