@InProceedings{li-baldwin-cohn:2018:N18-2,
  author    = {Li, Yitong  and  Baldwin, Timothy  and  Cohn, Trevor},
  title     = {What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {474--479},
  abstract  = {Most real world language problems require learning from heterogenous corpora, raising the problem of learning robust models which generalise well to both similar (\emph{in domain}) and dissimilar (\emph{out of domain}) instances to those seen in training. This requires learning an underlying task, while not learning irrelevant signals and biases specific to individual domains. We propose a novel method to optimise both in- and out-of-domain accuracy based on joint learning of a structured neural model with domain-specific and domain-general components, coupled with adversarial training for domain. Evaluating on multi-domain language identification and multi-domain sentiment analysis, we show substantial improvements over standard domain adaptation techniques, and domain-adversarial training.},
  url       = {http://www.aclweb.org/anthology/N18-2076}
}