@inproceedings{e051964d347047219214797318cdeeb8,
title = "IlliniMet: Illinois system for metaphor detection with contextual and linguistic information",
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.",
author = "Hongyu Gong and Kshitij Gupta and Akriti Jain and Suma Bhat",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics.; 2nd Workshop on Figurative Language Processing 2020 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 ; Conference date: 09-07-2020",
year = "2020",
doi = "10.18653/v1/P17",
language = "English (US)",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "146--153",
booktitle = "ACL 2020 - Figurative Language Processing, Proceedings of the 2nd Workshop",
}