@InProceedings{fu-EtAl:2017:I17-2,
  author    = {Fu, Lisheng  and  Nguyen, Thien Huu  and  Min, Bonan  and  Grishman, Ralph},
  title     = {Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {425--429},
  abstract  = {Relations are expressed in many domains such as newswire, weblogs and phone
	conversations. Trained on a source domain, a relation extractor's performance
	degrades when applied to target domains other than the source. A common yet
	labor-intensive method for domain adaptation is to construct a
	target-domain-specific labeled dataset for adapting the extractor. In response,
	we present an unsupervised domain adaptation method which only requires labels
	from the source domain. Our method is a joint model consisting of a CNN-based
	relation classifier and a domain-adversarial classifier. The two components are
	optimized jointly to learn a domain-independent representation for prediction
	on the target domain. Our model outperforms the state-of-the-art on all three
	test domains of ACE 2005.},
  url       = {http://www.aclweb.org/anthology/I17-2072}
}