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        <title>Don’t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities</title>
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    <abstract>In this paper, we introduce a new annotated dataset which is aimed at supporting the development of NLP models to identify and categorize language that is patronizing or condescending towards vulnerable communities (e.g. refugees, homeless people, poor families). While the prevalence of such language in the general media has long been shown to have harmful effects, it differs from other types of harmful language, in that it is generally used unconsciously and with good intentions. We furthermore believe that the often subtle nature of patronizing and condescending language (PCL) presents an interesting technical challenge for the NLP community. Our analysis of the proposed dataset shows that identifying PCL is hard for standard NLP models, with language models such as BERT achieving the best results.</abstract>
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%0 Conference Proceedings
%T Don’t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities
%A Perez Almendros, Carla
%A Espinosa Anke, Luis
%A Schockaert, Steven
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F perez-almendros-etal-2020-dont
%X In this paper, we introduce a new annotated dataset which is aimed at supporting the development of NLP models to identify and categorize language that is patronizing or condescending towards vulnerable communities (e.g. refugees, homeless people, poor families). While the prevalence of such language in the general media has long been shown to have harmful effects, it differs from other types of harmful language, in that it is generally used unconsciously and with good intentions. We furthermore believe that the often subtle nature of patronizing and condescending language (PCL) presents an interesting technical challenge for the NLP community. Our analysis of the proposed dataset shows that identifying PCL is hard for standard NLP models, with language models such as BERT achieving the best results.
%R 10.18653/v1/2020.coling-main.518
%U https://aclanthology.org/2020.coling-main.518
%U https://doi.org/10.18653/v1/2020.coling-main.518
%P 5891-5902

Markdown (Informal)

[Don’t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities](https://aclanthology.org/2020.coling-main.518) (Perez Almendros et al., COLING 2020)

• Don’t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities (Perez Almendros et al., COLING 2020)

ACL

• Carla Perez Almendros, Luis Espinosa Anke, and Steven Schockaert. 2020. Don’t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5891–5902, Barcelona, Spain (Online). International Committee on Computational Linguistics.