@InProceedings{das-EtAl:2017:Short,
  author    = {Das, Rajarshi  and  Zaheer, Manzil  and  Reddy, Siva  and  McCallum, Andrew},
  title     = {Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks},
  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     = {358--365},
  abstract  = {Existing question answering methods infer answers either from a knowledge base
	or from raw text. 
	While knowledge base (KB) methods are good at answering compositional
	questions, their performance is often affected by the incompleteness of the KB.
	Au contraire, 
	web text contains millions of facts that are absent in the KB, however in an
	unstructured form. Universal schema can support reasoning on the union of
	both structured KBs and unstructured text by aligning them in a common embedded
	space. In this paper we extend universal schema to natural language question
	answering, employing Memory networks to attend to the large body of
	facts in the combination of text and KB.
	Our models can be trained in an end-to-end fashion on question-answer pairs.
	Evaluation results on Spades fill-in-the-blank question answering dataset show
	that exploiting universal schema for question answering is better than using
	either a KB or text alone. 
	This model also outperforms the current state-of-the-art by 8.5 F1 points.},
  url       = {http://aclweb.org/anthology/P17-2057}
}