@inproceedings{alnafesah-etal-2020-augmenting,
    title = "Augmenting Neural Metaphor Detection with Concreteness",
    author = "Alnafesah, Ghadi  and
      Tayyar Madabushi, Harish  and
      Lee, Mark",
    editor = "Klebanov, Beata Beigman  and
      Shutova, Ekaterina  and
      Lichtenstein, Patricia  and
      Muresan, Smaranda  and
      Wee, Chee  and
      Feldman, Anna  and
      Ghosh, Debanjan",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.figlang-1.28/",
    doi = "10.18653/v1/2020.figlang-1.28",
    pages = "204--210",
    abstract = "The idea that a shift in concreteness within a sentence indicates the presence of a metaphor has been around for a while. However, recent methods of detecting metaphor that have relied on deep neural models have ignored concreteness and related psycholinguistic information. We hypothesis that this information is not available to these models and that their addition will boost the performance of these models in detecting metaphor. We test this hypothesis on the Metaphor Detection Shared Task 2020 and find that the addition of concreteness information does in fact boost deep neural models. We also run tests on data from a previous shared task and show similar results."
}