@InProceedings{kim-stratos-kim:2017:Long2,
  author    = {Kim, Young-Bum  and  Stratos, Karl  and  Kim, Dongchan},
  title     = {Adversarial Adaptation of Synthetic or Stale Data},
  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     = {1297--1307},
  abstract  = {Two types of data shift common in practice are 1. transferring from synthetic
	data to live user data (a deployment shift), and
	2. transferring from stale data to current data (a temporal shift). Both cause
	a distribution mismatch between training and evaluation, leading to a model
	that overfits the flawed training data and performs poorly on the test data. We
	propose a solution to this mismatch problem by framing it as domain adaptation,
	treating the flawed training dataset as a source domain and
	the evaluation dataset as a target domain. To this end, we use and build on
	several recent advances in neural domain adaptation such as adversarial
	training (Ganinet al., 2016) and domain separation network (Bousmalis et al.,
	2016), proposing a new effective adversarial training scheme. In both
	supervised and unsupervised adaptation scenarios, our approach yields clear
	improvement over strong baselines.},
  url       = {http://aclweb.org/anthology/P17-1119}
}