A Report on the Euphemisms Detection Shared Task
Patrick Lee, Anna Feldman, Jing Peng

TL;DR
This paper reports on a shared task focused on detecting euphemisms in text, analyzing participant methods and results to advance understanding of figurative language processing.
Contribution
It introduces a shared task dataset and benchmarks for euphemism detection, fostering research in figurative language understanding.
Findings
Participants used diverse NLP methods for euphemism detection
The shared task revealed key challenges in identifying euphemisms
Results highlight the effectiveness of certain modeling approaches
Abstract
This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (FigLang 2022) held in conjunction with EMNLP 2022. Participants were invited to investigate the euphemism detection task: given input text, identify whether it contains a euphemism. The input data is a corpus of sentences containing potentially euphemistic terms (PETs) collected from the GloWbE corpus (Davies and Fuchs, 2015), and are human-annotated as containing either a euphemistic or literal usage of a PET. In this paper, we present the results and analyze the common themes, methods and findings of the participating teams
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHate Speech and Cyberbullying Detection · Swearing, Euphemism, Multilingualism · Authorship Attribution and Profiling
