  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.
