Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities
This addresses the challenge of detecting subtle harmful language in media for NLP researchers, though it is incremental as it focuses on dataset creation.
The paper tackles the problem of identifying patronizing and condescending language (PCL) towards vulnerable communities by introducing a new annotated dataset, showing that standard NLP models struggle with this task, with BERT achieving the best results.
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.