@InProceedings{wiese-weissenborn-neves:2017:BioNLP17,
  author    = {Wiese, Georg  and  Weissenborn, Dirk  and  Neves, Mariana},
  title     = {Neural Question Answering at BioASQ 5B},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {76--79},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-2309}
}