IlliniMet: Illinois System for Metaphor Detection with Contextual and Linguistic Information

Hongyu Gong, Kshitij Gupta, Akriti Jain, Suma Bhat


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.
Anthology ID:
2020.figlang-1.21
Volume:
Proceedings of the Second Workshop on Figurative Language Processing
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | Fig-Lang | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
146–153
Language:
URL:
https://www.aclweb.org/anthology/2020.figlang-1.21
DOI:
10.18653/v1/2020.figlang-1.21
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/2020.figlang-1.21.pdf
Video:
 http://slideslive.com/38929719