@inproceedings{bizzoni-lappin-2018-predicting,
    title = "Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks",
    author = "Bizzoni, Yuri  and
      Lappin, Shalom",
    editor = "Beigman Klebanov, Beata  and
      Shutova, Ekaterina  and
      Lichtenstein, Patricia  and
      Muresan, Smaranda  and
      Wee, Chee",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-0906/",
    doi = "10.18653/v1/W18-0906",
    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."
}