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

Source: [SPBERTQA: A Two-Stage Question Answering System Based on Sentence Transformers for Medical Texts](<https://www.sota2.com/research/paper/68fa6cdb1e209131cea36551>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:54.087Z

Authors: Nhung Thi-Hong Nguyen, Phuong Phan-Dieu Ha, Luan Thanh Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen

Published: 2022-06-20T07:07:59.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2206.09600>)

[PDF](<https://arxiv.org/pdf/2206.09600.pdf>)