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

arXiv: https://arxiv.org/abs/2206.09600 · HTML: https://mlsanity.com/paper/2206.09600/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [VnCoreNLP: A Vietnamese Natural Language Processing Toolkit](https://mlsanity.com/paper/1801.01331/) (2018)
- [Learning-to-Rank with BERT in TF-Ranking](https://mlsanity.com/paper/2004.08476/) (2020)
- [CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension](https://mlsanity.com/paper/1803.09720/) (2018)
- [Medical Exam Question Answering with Large-scale Reading Comprehension](https://mlsanity.com/paper/1802.10279/) (2018)
- [New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles](https://mlsanity.com/paper/2006.11138/) (2020)

## Built on by (most cited first, 5 of 5)

- [ViMMRC 2.0 -- Enhancing Machine Reading Comprehension on Vietnamese Literature Text](https://mlsanity.com/paper/2303.18162/) (2023)
- [Contrastive Learning-based Sentence Encoders Implicitly Weight Informative Words](https://mlsanity.com/paper/2310.15921/) (2023)
- [MedPT: A Massive Medical Question Answering Dataset for Brazilian-Portuguese Speakers](https://mlsanity.com/paper/2511.11878/) (2025)
- [XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering](https://mlsanity.com/paper/2312.03567/) (2023)
- [A Benchmark Dataset and Evaluation Framework for Vietnamese Large Language Models in Customer Support](https://mlsanity.com/paper/2507.22542/) (2025)

## Abstract (arXiv)

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.