Don’t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities

Proceedings of the 28th International Conference on Computational Linguistics, pages 5891–5902 Barcelona, Spain (Online), December 8-13, 2020 5891 Don’t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities Carla P´erez-Almendros Luis Espinosa-Anke Steven Schockaert School of Computer Science and Informatics Cardiff University, United Kingdom {perezalmendrosc,espinosa-ankel,schockaerts1}@cardiff.ac.uk 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 anal- ysis of the proposed dataset shows that identifying PCL is hard for standard NLP models, with language models such as BERT achieving the best results. 1 Introduction In this paper, we analyze the use of Patronizing and Condescendig Language (PCL) towards vulnerable communities in the media. An entity engages in PCL when its language use shows a superior attitude towards others or depicts them in a compassionate way. This effect is not always conscious and the intention of the author is often to help the person or group they refer to (e.g. by raising awareness or funds, or moving the audience to action). However, these superior attitudes and a discourse of pity can routinize discrimination and make it less visible (Ng, 2007). Moreover, general media publications reach a large audience and we believe that unfair treatment of vulnerable groups in such media might lead to greater exclusion and inequalities. While there has been substantial work on modelling language that purposefully undermines others, e.g. offensive language or hate speech (Zampieri et al., 2019; Basile et al., 2019), the modelling of PCL is still an emergent area of study in NLP. Some reasons for this might include that the use of PCL in the media is commonly unconscious, subtler and more subjective than the types of discourse that are typically targeted in NLP. Specifically, a special focus in PCL towards vulnerable communities has not been yet considered, to the best of our knowledge. Within a broader setting, there has been some work on PCL which is concerned with the communica- tion between two parties, where one is patronized by the other, such as in social media interactions. In particular, Wang and Potts (2019) recently published the Talkdown corpus for condescension detection in comment-reply pairs from Reddit. In this work, the authors highlight the difficulty of the task and the need for a high-quality dataset annotated by experts, which is the approach we take for studying PCL towards vulnerable communities. To encourage more research on detecting PCL language, we introduce the Don’t Patronize Me! dataset1. This dataset contains more than 10,000 paragraphs extracted …

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