@inproceedings{fu-etal-2017-domain,
    title = "Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network",
    author = "Fu, Lisheng  and
      Nguyen, Thien Huu  and
      Min, Bonan  and
      Grishman, Ralph",
    editor = "Kondrak, Greg  and
      Watanabe, Taro",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://preview.aclanthology.org/landing_page/I17-2072/",
    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."
}