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This paper describes our submission to the 2017 BioASQ challenge. Weparticipated in Task B, Phase B which is concerned with biomedical questionanswering (QA). We focus on factoid and list question, using an extractive QAmodel, that is, we restrict our system to output substrings of the providedtext snippets. At the core of our system, we use FastQA, a state-of-the-artneural QA system. We extended it with biomedical word embeddings and changedits answer layer to be able to answer list questions in addition to factoidquestions. 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 ourapproach, we achieve state-of-the-art results on factoid questions andcompetitive results on list questions.

Neural Question Answering at BioASQ 5B