Summary of Banglaembed: Efficient Sentence Embedding Models For a Low-resource Language Using Cross-lingual Distillation Techniques, by Muhammad Rafsan Kabir et al.
BanglaEmbed: Efficient Sentence Embedding Models for a Low-Resource Language Using Cross-Lingual Distillation Techniques
by Muhammad Rafsan Kabir, Md. Mohibur Rahman Nabil, Mohammad Ashrafuzzaman Khan
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents two lightweight sentence transformer models for Bengali, a low-resource language with over 230 million speakers. The proposed approach leverages cross-lingual knowledge distillation from a pre-trained English sentence transformer to create effective models for tasks like paraphrase detection, semantic textual similarity, and Bangla hate speech detection. The novel method consistently outperforms existing Bangla sentence transformers across multiple downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces two new sentence transformer models for Bengali, a language that is still under-explored despite being spoken by millions of people. These models are designed to work well even in places with limited resources. They use knowledge from a powerful English model to help them make good predictions. The paper tests these models on tasks like identifying similar texts and detecting hate speech. The results show that the new models do better than other Bengali sentence transformers. |
Keywords
» Artificial intelligence » Knowledge distillation » Transformer
