# Neural Question Answering at BioASQ 5B

arXiv: https://arxiv.org/abs/1706.08568 · HTML: https://mlsanity.com/paper/1706.08568/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2017.

## Built on by (most cited first, 4 of 4)

- [ELECTRAMed: a new pre-trained language representation model for biomedical NLP](https://mlsanity.com/paper/2104.09585/) (2021)
- [Towards a Medical AI Scientist](https://mlsanity.com/paper/2603.28589/) (2026)
- [Question answering systems for health professionals at the point of care -- a systematic review](https://mlsanity.com/paper/2402.01700/) (2024)
- [Using Weak Supervision and Data Augmentation in Question Answering](https://mlsanity.com/paper/2309.16175/) (2023)

## Abstract (arXiv)

This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). We focus on factoid and list question, using an extractive QA model, that is, we restrict our system to output substrings of the provided text snippets. At the core of our system, we use FastQA, a state-of-the-art neural QA system. We extended it with biomedical word embeddings and changed its answer layer to be able to answer list questions in addition to factoid questions. We pre-trained the model on a large-scale open-domain QA dataset, SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our approach, we achieve state-of-the-art results on factoid questions and competitive results on list questions.