TalkDown: A Corpus for Condescension Detection in Context

By Zijian Wang, Christopher Potts. 2019

55 citations recorded by the source corpus.

Condescending language use is caustic; it can bring dialogues to an end and bifurcate communities. Thus, systems for condescension detection could have a large positive impact. A challenge here is that condescension is often impossible to detect from isolated utterances, as it depends on the discourse and social context. To address this, we present TalkDown, a new labeled dataset of condescending linguistic acts in context. We show that extending a language-only model with representations of the discourse improves performance, and we motivate techniques for dealing with the low rates of condescension overall. We also use our model to estimate condescension rates in various online communities and relate these differences to differing community norms.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (2023, background): “…including explicit insults (Wulczyn et al., 2017; David-son et al., 2017; Zampieri et al., 2019; Rosen-thal et al., 2021) or more covert offenses (Wang and Potts, 2019; Breitfeller et al., 2019; Han and Tsvetkov, 2020; Price et al., 2020), the interaction between LMs and users is growing more…”
Social Bias Frames: Reasoning about Social and Power Implications of Language (2019, neutral): “, 2019) and condescension (Wang and Potts, 2019).”
A Framework for the Computational Linguistic Analysis of Dehumanization (2020, background): “There has also been a growing focus on the analysis of subtle manifestations of social biases, such as condescension (Wang and Potts, 2019), microagressions (Breitfeller et al.”
Unsupervised Discovery of Implicit Gender Bias (2020, methodology): “Our work differs from prior work on bias detection in NLP in that we infer bias from data in an unsupervised way, whereas prior work relies on crowd-sourced annotations (Fast et al., 2016; Bolukbasi et al., 2016; Wang and Potts, 2019; Sap et al., 2020).”
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2021, unknown): “, 2019) 3 Offensiveness 2 CS (Wang and Potts, 2019) 3 Condescending 2 SMP (Gehman et al.”