论文标题
Spbertqa：基于医学文本的句子变形金刚的两个阶段问答系统
SPBERTQA: A Two-Stage Question Answering System Based on Sentence Transformers for Medical Texts
论文作者
论文摘要
近年来，问题回答（QA）系统引起了爆炸性的关注。但是，越南语中的质量检查任务没有很多数据集。值得注意的是，医疗领域中大多没有数据集。因此，我们为回答数据集（VIHealthQA）建立了一个越南医疗保健问题，其中包括10,015个问题 - 答案通过这项任务，其中在享有盛名的健康网站上问了来自健康障碍用户的问题，并在享有资格的专家中提出了答案。本文提出了一个基于句子 - 伯特（Sbert）的两阶段质量检查系统，使用多个负损失（MNR）损失与BM25结合在一起。然后，我们使用许多单词范围的模型进行了不同的实验，以评估系统的性能。通过获得的结果，该系统的性能比传统方法更好。
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.
