BanglaEmbed: Efficient Sentence Embedding Models for a Low-Resource Language Using Cross-Lingual Distillation Techniques

• URL: http://arxiv.org/abs/2411.15270v1
• Date: Fri, 22 Nov 2024 13:03:25 GMT
• Title: BanglaEmbed: Efficient Sentence Embedding Models for a Low-Resource Language Using Cross-Lingual Distillation Techniques
• Authors: Muhammad Rafsan Kabir, Md. Mohibur Rahman Nabil, Mohammad Ashrafuzzaman Khan,
• Abstract summary: This work introduces two lightweight sentence transformers for the Bangla language. This method distills knowledge from a pre-trained, high-performing English sentence transformer. The new method consistently outperformed existing Bangla sentence transformers.
• Score: 0.0
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• Abstract: Sentence-level embedding is essential for various tasks that require understanding natural language. Many studies have explored such embeddings for high-resource languages like English. However, low-resource languages like Bengali (a language spoken by almost two hundred and thirty million people) are still under-explored. This work introduces two lightweight sentence transformers for the Bangla language, leveraging a novel cross-lingual knowledge distillation approach. This method distills knowledge from a pre-trained, high-performing English sentence transformer. Proposed models are evaluated across multiple downstream tasks, including paraphrase detection, semantic textual similarity (STS), and Bangla hate speech detection. The new method consistently outperformed existing Bangla sentence transformers. Moreover, the lightweight architecture and shorter inference time make the models highly suitable for deployment in resource-constrained environments, making them valuable for practical NLP applications in low-resource languages.

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