@InProceedings{liu-EtAl:2017:Long1,
  author    = {Liu, Ting  and  Cui, Yiming  and  Yin, Qingyu  and  Zhang, Wei-Nan  and  Wang, Shijin  and  Hu, Guoping},
  title     = {Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution},
  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     = {102--111},
  abstract  = {Most existing approaches for zero pronoun resolution are heavily relying on
	annotated data, which is often released by shared task organizers.
	Therefore, the lack of annotated data becomes a major obstacle in the progress
	of zero pronoun resolution task. 
	Also, it is expensive to spend manpower on labeling the data for better
	performance.
	To alleviate the problem above, in this paper, we propose a simple but novel
	approach to automatically generate large-scale pseudo training data for zero
	pronoun resolution.
	Furthermore, we successfully transfer the cloze-style reading comprehension
	neural network model into zero pronoun resolution task and propose a two-step
	training mechanism to overcome the gap between the pseudo training data and the
	real one.
	Experimental results show that the proposed approach significantly outperforms
	the state-of-the-art systems with an absolute improvements of 3.1\% F-score on
	OntoNotes 5.0 data.},
  url       = {http://aclweb.org/anthology/P17-1010}
}