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.

本文描述了我们在 2017 年 BioASQ 挑战赛中的参与，并重点关注生物医学问题回答技术。我们使用了一种抽取式 QA 模型，使用生物医学词向量来扩展 FastQA，并将其应用于提取输入文本片段的子字符串输出。我们将模型在大规模开放域 QA 数据集 SQuAD 上进行预训练，然后在 BioASQ 训练集上进行微调参数。通过我们的方法，我们在事实问题上实现了最先进的结果，并在列表问题上实现了有竞争力的结果。