@InProceedings{golub-EtAl:2017:EMNLP2017,
  author    = {Golub, David  and  Huang, Po-Sen  and  He, Xiaodong  and  Deng, Li},
  title     = {Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {835--844},
  abstract  = {We develop a technique for transfer learning in machine comprehension (MC)
	using a novel two-stage synthesis network.  Given a high performing MC model in
	one domain, our technique aims to answer questions about documents in another
	domain, where we use no labeled data of question-answer pairs. Using the
	proposed synthesis network with a pretrained model on the SQuAD dataset, we
	achieve an F1 measure of 46.6% on the challenging NewsQA dataset, approaching
	performance of in-domain models (F1 measure of 50.0%) and outperforming the
	out-of-domain baseline by 7.6%, without use of provided annotations.},
  url       = {https://www.aclweb.org/anthology/D17-1087}
}