@inproceedings{gong-etal-2020-illinimet,
    title = "{I}llini{M}et: {I}llinois System for Metaphor Detection with Contextual and Linguistic Information",
    author = "Gong, Hongyu  and
      Gupta, Kshitij  and
      Jain, Akriti  and
      Bhat, Suma",
    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/add-emnlp-2024-awards/2020.figlang-1.21/",
    doi = "10.18653/v1/2020.figlang-1.21",
    pages = "146--153",
    abstract = "Metaphors are rhetorical use of words based on the conceptual mapping as opposed to their literal use. Metaphor detection, an important task in language understanding, aims to identify metaphors in word level from given sentences. We present IlliniMet, a system to automatically detect metaphorical words. Our model combines the strengths of the contextualized representation by the widely used RoBERTa model and the rich linguistic information from external resources such as WordNet. The proposed approach is shown to outperform strong baselines on a benchmark dataset. Our best model achieves F1 scores of 73.0{\%} on VUA ALLPOS, 77.1{\%} on VUA VERB, 70.3{\%} on TOEFL ALLPOS and 71.9{\%} on TOEFL VERB."
}