An Exploration of Linguistically-Driven and Transfer Learning Methods for Euphemism Detection

Devika Tiwari, Natalie Parde


Abstract
Euphemisms are often used to drive rhetoric, but their automated recognition and interpretation are under-explored. We investigate four methods for detecting euphemisms in sentences containing potentially euphemistic terms. The first three linguistically-motivated methods rest on an understanding of (1) euphemism’s role to attenuate the harsh connotations of a taboo topic and (2) euphemism’s metaphorical underpinnings. In contrast, the fourth method follows recent innovations in other tasks and employs transfer learning from a general-domain pre-trained language model. While the latter method ultimately (and perhaps surprisingly) performed best (F1 = 0.74), we comprehensively evaluate all four methods to derive additional useful insights from the negative results.
Anthology ID:
2022.flp-1.18
Volume:
Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Debanjan Ghosh, Beata Beigman Klebanov, Smaranda Muresan, Anna Feldman, Soujanya Poria, Tuhin Chakrabarty
Venue:
Fig-Lang
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
131–136
Language:
URL:
https://aclanthology.org/2022.flp-1.18
DOI:
10.18653/v1/2022.flp-1.18
Bibkey:
Cite (ACL):
Devika Tiwari and Natalie Parde. 2022. An Exploration of Linguistically-Driven and Transfer Learning Methods for Euphemism Detection. In Proceedings of the 3rd Workshop on Figurative Language Processing (FLP), pages 131–136, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
An Exploration of Linguistically-Driven and Transfer Learning Methods for Euphemism Detection (Tiwari & Parde, Fig-Lang 2022)
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PDF:
https://aclanthology.org/2022.flp-1.18.pdf