United Arab Emirates

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.

EMNLP 2022

Identifying Condescending Language: A Tale of Two Distinct Phenomena?

patronizing and condescending language

text classification

bias

workshop paper

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Identifying Condescending Language: A Tale of Two Distinct Phenomena?

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Identifying Condescending Language: A Tale of Two Distinct Phenomena?

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Next from EMNLP 2022

Generate Me a Bedtime Story: Leveraging Natural Language Processing for Early Vocabulary Enhancement

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PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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