A Report on the Euphemisms Detection Shared Task
This addresses the problem of detecting euphemisms in natural language processing for researchers and practitioners, but it is incremental as it builds on existing tasks and datasets.
The paper reports on a shared task for euphemism detection, where participants developed methods to identify euphemisms in text using a human-annotated corpus, and it presents results and analysis of the approaches used.
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