@InProceedings{chen-EtAl:2017:Long4,
  author    = {Chen, Danqi  and  Fisch, Adam  and  Weston, Jason  and  Bordes, Antoine},
  title     = {Reading Wikipedia to Answer Open-Domain Questions},
  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     = {1870--1879},
  abstract  = {This paper proposes to tackle open-domain question answering using Wikipedia as
	the unique knowledge source: the answer to any factoid question is a text span
	in a Wikipedia article. This task of machine reading at scale combines the
	challenges of document retrieval (finding the relevant articles) with that of
	machine comprehension of text (identifying the answer spans from those
	articles). Our approach combines a search component based on bigram hashing and
	TF-IDF matching with a multi-layer recurrent neural network model trained to
	detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA
	datasets indicate that (1) both modules are highly competitive with respect to
	existing counterparts and (2) multitask learning using distant supervision on
	their combination is an effective complete system on this challenging task.},
  url       = {http://aclweb.org/anthology/P17-1171}
}