A Report on the Euphemisms Detection Shared Task

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