# Neural Question Answering at BioASQ 5B

## TL;DR

This paper presents a neural question answering system for biomedical questions, extending FastQA with biomedical embeddings and list-answer capabilities, achieving state-of-the-art results on factoid questions in BioASQ 2017.

## Contribution

We adapted FastQA for biomedical QA by integrating biomedical embeddings and supporting list questions, with pre-training on SQuAD and fine-tuning on BioASQ data.

## Key findings

- Achieved state-of-the-art results on factoid questions.
- Obtained competitive results on list questions.
- Demonstrated effectiveness of transfer learning from open-domain QA.

## Abstract

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.