Question-Answering System for Bangla: Fine-tuning BERT-Bangla for a Closed Domain
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912060117352448 |
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| author | Roy, Subal Chandra Manik, Md Motaleb Hossen |
| author_facet | Roy, Subal Chandra Manik, Md Motaleb Hossen |
| contents | Question-answering systems for Bengali have seen limited development, particularly in domain-specific applications. Leveraging advancements in natural language processing, this paper explores a fine-tuned BERT-Bangla model to address this gap. It presents the development of a question-answering system for Bengali using a fine-tuned BERT-Bangla model in a closed domain. The dataset was sourced from Khulna University of Engineering \& Technology's (KUET) website and other relevant texts. The system was trained and evaluated with 2500 question-answer pairs generated from curated data. Key metrics, including the Exact Match (EM) score and F1 score, were used for evaluation, achieving scores of 55.26\% and 74.21\%, respectively. The results demonstrate promising potential for domain-specific Bengali question-answering systems. Further refinements are needed to improve performance for more complex queries. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_03923 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Question-Answering System for Bangla: Fine-tuning BERT-Bangla for a Closed Domain Roy, Subal Chandra Manik, Md Motaleb Hossen Computation and Language Question-answering systems for Bengali have seen limited development, particularly in domain-specific applications. Leveraging advancements in natural language processing, this paper explores a fine-tuned BERT-Bangla model to address this gap. It presents the development of a question-answering system for Bengali using a fine-tuned BERT-Bangla model in a closed domain. The dataset was sourced from Khulna University of Engineering \& Technology's (KUET) website and other relevant texts. The system was trained and evaluated with 2500 question-answer pairs generated from curated data. Key metrics, including the Exact Match (EM) score and F1 score, were used for evaluation, achieving scores of 55.26\% and 74.21\%, respectively. The results demonstrate promising potential for domain-specific Bengali question-answering systems. Further refinements are needed to improve performance for more complex queries. |
| title | Question-Answering System for Bangla: Fine-tuning BERT-Bangla for a Closed Domain |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.03923 |