@InProceedings{min-seo-hajishirzi:2017:Short,
  author    = {Min, Sewon  and  Seo, Minjoon  and  Hajishirzi, Hannaneh},
  title     = {Question Answering through Transfer Learning from Large Fine-grained Supervision Data},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {510--517},
  abstract  = {We show that the task of question answering (QA) can significantly benefit from
	the transfer learning of models trained on a different large, fine-grained QA
	dataset. We achieve the state of the art in two well-studied QA datasets,
	WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique
	from SQuAD. For WikiQA, our model outperforms the previous best model by more
	than 8%. We demonstrate that finer supervision provides better guidance for
	learning lexical and syntactic information than coarser supervision, through
	quantitative results and visual analysis. We also show that a similar transfer
	learning procedure  achieves  the state of the art on an entailment task.},
  url       = {http://aclweb.org/anthology/P17-2081}
}