@InProceedings{issa-EtAl:2018:N18-1,
  author    = {Issa, Fuad  and  Damonte, Marco  and  Cohen, Shay B.  and  Yan, Xiaohui  and  Chang, Yi},
  title     = {Abstract Meaning Representation for Paraphrase Detection},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {442--452},
  abstract  = {Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal for paraphrase detection, a problem in which one is required to specify whether two sentences have the same meaning. We show that na\"{i}ve use of AMR in paraphrase detection is not necessarily useful, and turn to describe a technique based on latent semantic analysis in combination with AMR parsing that significantly advances state-of-the-art results in paraphrase detection for the Microsoft Research Paraphrase Corpus. Our best results in the transductive setting are 86.6\% for accuracy and 90.0\% for F$\_1$ measure.},
  url       = {http://www.aclweb.org/anthology/N18-1041}
}