@InProceedings{khot-sabharwal-clark:2017:Short,
  author    = {Khot, Tushar  and  Sabharwal, Ashish  and  Clark, Peter},
  title     = {Answering Complex Questions Using Open Information Extraction},
  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     = {311--316},
  abstract  = {While there has been substantial progress in factoid question-answering (QA),
	answering complex questions remains challenging, typically requiring both a
	large body of knowledge and inference techniques. Open Information Extraction
	(Open IE) provides a way to generate semi-structured knowledge for QA, but to
	date such knowledge has only been used to answer simple questions with
	retrieval-based methods. We overcome this limitation by presenting a method for
	reasoning with Open IE knowledge, allowing more complex questions to be
	handled. Using a recently proposed support graph optimization framework for QA,
	we develop a new inference model for Open IE, in particular one that can work
	effectively with multiple short facts, noise, and the relational structure of
	tuples. Our model significantly outperforms a state-of-the-art structured
	solver on complex questions of varying difficulty, while also removing the
	reliance on manually curated knowledge.},
  url       = {http://aclweb.org/anthology/P17-2049}
}