@InProceedings{hsi-EtAl:2016:COLING,
  author    = {Hsi, Andrew  and  Yang, Yiming  and  Carbonell, Jaime  and  Xu, Ruochen},
  title     = {Leveraging Multilingual Training for Limited Resource Event Extraction},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1201--1210},
  abstract  = {Event extraction has become one of the most important topics in information
	extraction, but to date, there is very limited work on leveraging cross-lingual
	training to boost performance. We propose a new event extraction approach that
	trains on multiple languages using a combination of both language-dependent and
	language-independent features, with particular focus on the case where target
	domain training data is of very limited size. We show empirically that
	multilingual training can boost performance for the tasks of event trigger
	extraction and event argument extraction on the Chinese ACE 2005 dataset.},
  url       = {http://aclweb.org/anthology/C16-1114}
}