@InProceedings{peng-dredze:2017:RepL4NLP,
  author    = {Peng, Nanyun  and  Dredze, Mark},
  title     = {Multi-task Domain Adaptation for Sequence Tagging},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {91--100},
  abstract  = {Many domain adaptation approaches rely
	on learning cross domain shared representations
	to transfer the knowledge learned
	in one domain to other domains. Traditional
	domain adaptation only considers
	adapting for one task. In this paper, we
	explore multi-task representation learning
	under the domain adaptation scenario. We
	propose a neural network framework that
	supports domain adaptation for multiple
	tasks simultaneously, and learns shared
	representations that better generalize for
	domain adaptation. We apply the proposed
	framework to domain adaptation
	for sequence tagging problems considering
	two tasks: Chinese word segmentation
	and named entity recognition. Experiments
	show that multi-task domain adaptation
	works better than disjoint domain
	adaptation for each task, and achieves the
	state-of-the-art results for both tasks in the
	social media domain.},
  url       = {http://www.aclweb.org/anthology/W17-2612}
}