SPBERTQA: A Two-Stage Question Answering System Based on Sentence Transformers for Medical Texts

• URL: http://arxiv.org/abs/2206.09600v1
• Date: Mon, 20 Jun 2022 07:07:59 GMT
• Title: SPBERTQA: A Two-Stage Question Answering System Based on Sentence Transformers for Medical Texts
• Authors: Nhung Thi-Hong Nguyen, Phuong Phan-Dieu Ha, Luan Thanh Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
• Abstract summary: This paper proposes a two-stage QA system based on Sentence-BERT (SBERT) using multiple negatives ranking (MNR) loss combined with BM25. With the obtained results, this system achieves better performance than traditional methods.
• Score: 2.5199066832791535
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Question answering (QA) systems have gained explosive attention in recent years. However, QA tasks in Vietnamese do not have many datasets. Significantly, there is mostly no dataset in the medical domain. Therefore, we built a Vietnamese Healthcare Question Answering dataset (ViHealthQA), including 10,015 question-answer passage pairs for this task, in which questions from health-interested users were asked on prestigious health websites and answers from highly qualified experts. This paper proposes a two-stage QA system based on Sentence-BERT (SBERT) using multiple negatives ranking (MNR) loss combined with BM25. Then, we conduct diverse experiments with many bag-of-words models to assess our system's performance. With the obtained results, this system achieves better performance than traditional methods.

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