@InProceedings{wiese-weissenborn-neves:2017:CoNLL,
  author    = {Wiese, Georg  and  Weissenborn, Dirk  and  Neves, Mariana},
  title     = {Neural Domain Adaptation for Biomedical Question Answering},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {281--289},
  abstract  = {Factoid question answering (QA) has recently benefited from the development of
	deep learning (DL) systems. Neural network models outperform traditional
	approaches in domains where large datasets exist, such as SQuAD (ca. 100,000
	questions) for Wikipedia articles. However, these systems have not yet been
	applied to QA in more specific domains, such as biomedicine, because datasets
	are generally too small to train a DL system from scratch. For example, the
	BioASQ dataset for biomedical QA comprises less then 900 factoid (single
	answer) and list (multiple answers) QA instances. In this work, we adapt a
	neural QA system trained on a large open-domain dataset (SQuAD, source) to a
	biomedical dataset (BioASQ, target) by employing various transfer learning
	techniques. Our network architecture is based on a state-of-the-art QA system,
	extended with biomedical word embeddings and a novel mechanism to answer list
	questions. In contrast to existing biomedical QA systems, our system does not
	rely on domain-specific ontologies, parsers or entity taggers, which are
	expensive to create. Despite this fact, our systems achieve state-of-the-art
	results on factoid questions and competitive results on list questions.},
  url       = {http://aclweb.org/anthology/K17-1029}
}