@InProceedings{reddy-EtAl:2017:EACLlong,
  author    = {Reddy, Sathish  and  Raghu, Dinesh  and  Khapra, Mitesh M.  and  Joshi, Sachindra},
  title     = {Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {376--385},
  abstract  = {In recent  years, knowledge  graphs  such  as Freebase that capture facts about
	entities and relationships  between  them  have  been  used actively  for 
	answering  factoid  questions. In this                    paper, we explore the
	problem of
	automatically generating question answer pairs from a given knowledge graph.
	The generated question answer (QA) pairs can be used in several downstream
	applications. For example, they could be used for training better QA systems.
	To generate such QA pairs, we first extract a set of keywords from entities and
	relationships expressed in a triple stored in the knowledge graph. From each
	such set, we use a subset of keywords to generate a natural language question
	that has a unique answer. We treat this subset of keywords as a sequence and
	propose a sequence to sequence model using RNN to generate a natural language
	question from it. Our RNN based model generates QA pairs with an accuracy of
	33.61 percent and performs 110.47  percent (relative) better than  a 
	state-of-the-art template based method for generating natural language question
	from keywords. We also do an extrinsic evaluation by using the generated QA
	pairs to train a QA system and observe that the F1-score of the QA system
	improves by 5.5 percent (relative) when using automatically generated QA pairs
	in addition to manually generated QA pairs available for training.},
  url       = {http://www.aclweb.org/anthology/E17-1036}
}