# Sequence tagging for biomedical extractive question answering

arXiv: https://arxiv.org/abs/2104.07535 · HTML: https://mlsanity.com/paper/2104.07535/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 48 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications](https://mlsanity.com/paper/1711.05073/) (2017)
- [Entity-Relation Extraction as Multi-Turn Question Answering](https://mlsanity.com/paper/1905.05529/) (2019)
- [Biomedical Entity Representations with Synonym Marginalization](https://mlsanity.com/paper/2005.00239/) (2020)
- [BIOMRC: A Dataset for Biomedical Machine Reading Comprehension](https://mlsanity.com/paper/2005.06376/) (2020)
- [Making Neural QA as Simple as Possible but not Simpler](https://mlsanity.com/paper/1703.04816/) (2017)
- [Pre-trained Language Model for Biomedical Question Answering](https://mlsanity.com/paper/1909.08229/) (2019)
- [Neural Domain Adaptation for Biomedical Question Answering](https://mlsanity.com/paper/1706.03610/) (2017)
- [Results of the seventh edition of the BioASQ Challenge](https://mlsanity.com/paper/2006.09174/) (2020)
- [CollaboNet: collaboration of deep neural networks for biomedical named entity recognition](https://mlsanity.com/paper/1809.07950/) (2018)
- [Transferability of Natural Language Inference to Biomedical Question Answering](https://mlsanity.com/paper/2007.00217/) (2020)

## Built on by (most cited first, 12 of 12)

- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
- [Overview of BioASQ 2021: The ninth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering](https://mlsanity.com/paper/2106.14885/) (2021)
- [LIQUID: A Framework for List Question Answering Dataset Generation](https://mlsanity.com/paper/2302.01691/) (2023)
- [DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections](https://mlsanity.com/paper/2508.15851/) (2025)
- [Explanatory Argument Extraction of Correct Answers in Resident Medical Exams](https://mlsanity.com/paper/2312.00567/) (2023)
- [LOCORE: Image Re-ranking with Long-Context Sequence Modeling](https://mlsanity.com/paper/2503.21772/) (2025)
- [Adapting Pre-trained Generative Models for Extractive Question Answering](https://mlsanity.com/paper/2311.02961/) (2023)
- [Development of an Extractive Clinical Question Answering Dataset with Multi-Answer and Multi-Focus Questions](https://mlsanity.com/paper/2201.02517/) (2022)
- [Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application](https://mlsanity.com/paper/2306.05323/) (2023)
- [Question answering systems for health professionals at the point of care -- a systematic review](https://mlsanity.com/paper/2402.01700/) (2024)
- [Evaluating List Construction and Temporal Understanding capabilities of Large Language Models](https://mlsanity.com/paper/2506.21783/) (2025)
- [Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026](https://mlsanity.com/paper/2606.27446/) (2026)

## Abstract (arXiv)

Current studies in extractive question answering (EQA) have modeled the single-span extraction setting, where a single answer span is a label to predict for a given question-passage pair. This setting is natural for general domain EQA as the majority of the questions in the general domain can be answered with a single span. Following general domain EQA models, current biomedical EQA (BioEQA) models utilize the single-span extraction setting with post-processing steps. In this article, we investigate the question distribution across the general and biomedical domains and discover biomedical questions are more likely to require list-type answers (multiple answers) than factoid-type answers (single answer). This necessitates the models capable of producing multiple answers for a question. Based on this preliminary study, we propose a sequence tagging approach for BioEQA, which is a multi-span extraction setting. Our approach directly tackles questions with a variable number of phrases as their answer and can learn to decide the number of answers for a question from training data. Our experimental results on the BioASQ 7b and 8b list-type questions outperformed the best-performing existing models without requiring post-processing steps. Source codes and resources are freely available for download at https://github.com/dmis-lab/SeqTagQA