Identifying condescending language: a tale of two distinct phenomena?

Perez Almendros, Carla ORCID: https://orcid.org/0000-0001-9360-4011 and Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 2022. Identifying condescending language: a tale of two distinct phenomena? Presented at: Second Workshop on NLP for Positive Impact (NLP4PI), Abu Dhabi, United Arab Emirates (Hybrid), 7 December 2022. Published in: Biester, Laura, Demszky, Dorottya, Jin, Zhijing, Sachan, Mrinmaya, Tetreault, Joel, Wilson, Steven, Xiao, Lu and Zhao, Jieyu eds. Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI). Association for Computational Linguistics, 130–141. 10.18653/v1/2022.nlp4pi-1.15

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Abstract

Patronizing and condescending language is characterized, among others, by its subtle nature. It thus seems reasonable to assume that detecting condescending language in text would be harder than detecting more explicitly harmful language, such as hate speech. However, the results of a SemEval-2022 Task devoted to this topic paint a different picture, with the top-performing systems achieving remarkably strong results. In this paper, we analyse the surprising effectiveness of standard text classification methods in more detail. In particular, we highlight the presence of two rather different types of condescending language in the dataset from the SemEval task. Some inputs are condescending because of the way they talk about a particular subject, i.e. condescending language in this case is a linguistic phenomenon, which can, in principle, be learned from training examples. However, other inputs are condescending because of the nature of what is said, rather than the way in which it is expressed, e.g. by emphasizing stereotypes about a given community. In such cases, our ability to detect condescending language, with current methods, largely depends on the presence of similar examples in the training data.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computational Linguistics
ISBN:	9781959429197
Related URLs:	Publisher
Last Modified:	13 Feb 2025 16:45
URI:	https://orca.cardiff.ac.uk/id/eprint/175718

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