@InProceedings{liu-EtAl:2017:Long2,
  author    = {Liu, Shulin  and  Chen, Yubo  and  Liu, Kang  and  Zhao, Jun},
  title     = {Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms},
  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     = {1789--1798},
  abstract  = {This paper tackles the task of event detection (ED), which involves identifying
	and categorizing events. We argue that arguments provide significant clues to
	this task, but they are either completely ignored or exploited in an indirect
	manner in existing detection approaches. In this work, we propose to exploit
	argument information explicitly for ED via supervised attention mechanisms. In
	specific, we systematically investigate the proposed model under the
	supervision of different attention strategies. Experimental results show that
	our approach advances state-of-the-arts and achieves the best F1 score on
	ACE 2005 dataset.},
  url       = {http://aclweb.org/anthology/P17-1164}
}