@InProceedings{wang-EtAl:2017:Long2,
  author    = {Wang, Wenhui  and  Yang, Nan  and  Wei, Furu  and  Chang, Baobao  and  Zhou, Ming},
  title     = {Gated Self-Matching Networks for Reading Comprehension and Question Answering},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {189--198},
  abstract  = {In this paper, we present the gated self-matching networks for reading
	comprehension style question answering, which aims to answer questions from a
	given passage. We first match the question and passage with gated
	attention-based recurrent networks to obtain the question-aware passage
	representation. Then we propose a self-matching attention mechanism to refine
	the representation by matching the passage against itself, which effectively
	encodes information from the whole passage. We finally employ the pointer
	networks to locate the positions of answers from the passages. We conduct
	extensive experiments on the SQuAD dataset. The single model achieves 71.3% on
	the evaluation metrics of exact match on the hidden test set, while the
	ensemble model further boosts the results to 75.9%. At the time of submission
	of the paper, our model holds the first place on the SQuAD leaderboard for both
	single and ensemble model.},
  url       = {http://aclweb.org/anthology/P17-1018}
}