@InProceedings{bizzoni-lappin:2018:W18-09,
  author    = {Bizzoni, Yuri  and  Lappin, Shalom},
  title     = {Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks},
  booktitle = {Proceedings of the Workshop on Figurative Language Processing},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {45--55},
  abstract  = {We propose a new annotated corpus for metaphor interpretation by paraphrase, and a novel DNN model for performing this task. Our corpus consists of 200 sets of 5 sentences, with each set containing one reference metaphorical sentence, and four ranked candidate paraphrases. Our model is trained for a binary classification of paraphrase candidates, and then used to predict graded paraphrase acceptability. It reaches an encouraging 75\% accuracy on the binary classification task, and high Pearson (.75) and Spearman (.68) correlations on the gradient judgment prediction task.},
  url       = {http://www.aclweb.org/anthology/W18-0906}
}