@InProceedings{mallinson-sennrich-lapata:2017:EACLlong,
  author    = {Mallinson, Jonathan  and  Sennrich, Rico  and  Lapata, Mirella},
  title     = {Paraphrasing Revisited with Neural Machine Translation},
  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     = {881--893},
  abstract  = {Recognizing and generating paraphrases is an important component in many
	natural language processing applications.  A well-established technique for
	automatically extracting paraphrases leverages bilingual corpora to find
	meaning-equivalent phrases in a single language by ``pivoting'' over a shared
	translation in another language. In this paper we revisit bilingual pivoting in
	the context of neural machine translation and present a paraphrasing model
	based purely on neural networks. Our model represents paraphrases in a
	continuous space, estimates the degree of semantic relatedness between text
	segments of arbitrary length, and generates candidate paraphrases for any
	source input. Experimental results across tasks and datasets show that neural
	paraphrases outperform those obtained with conventional phrase-based pivoting
	approaches.},
  url       = {http://www.aclweb.org/anthology/E17-1083}
}