@InProceedings{dong-EtAl:2017:EMNLP2017,
  author    = {Dong, Li  and  Mallinson, Jonathan  and  Reddy, Siva  and  Lapata, Mirella},
  title     = {Learning to Paraphrase for Question Answering},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {875--886},
  abstract  = {Question answering (QA) systems are sensitive to the many different ways
	natural language expresses the same information need. In this paper we turn to
	paraphrases as a means of capturing this knowledge and present a general
	framework which learns felicitous paraphrases for various QA tasks. Our method
	is trained end-to-end using question-answer pairs as a supervision signal. A
	question and its paraphrases serve as input to a neural scoring model which
	assigns higher weights to linguistic expressions most likely to yield correct
	answers. We evaluate our approach on QA over Freebase and answer sentence
	selection. Experimental results on three datasets show that our framework
	consistently improves performance, achieving competitive results despite the
	use of simple QA models.},
  url       = {https://www.aclweb.org/anthology/D17-1091}
}