Beyond Denouncing Hate: Strategies for Countering Implied Biases and
  Stereotypes in Language
Jimin Mun Emily Allaway Akhila Yerukola Laura Vianna Sarah-Jane Leslie Maarten Sap
Counterspeech, i.e., responses to counteract potential harms of hateful
speech, has become an increasingly popular solution to address online hate
speech without censorship. However, properly countering hateful language
requires countering and dispelling the underlying inaccurate stereotypes
implied by such language. In this work, we draw from psychology and philosophy
literature to craft six psychologically inspired strategies to challenge the
underlying stereotypical implications of hateful language. We first examine the
convincingness of each of these strategies through a user study, and then
compare their usages in both human- and machine-generated counterspeech
datasets. Our results show that human-written counterspeech uses countering
strategies that are more specific to the implied stereotype (e.g., counter
examples to the stereotype, external factors about the stereotype's origins),
whereas machine-generated counterspeech uses less specific strategies (e.g.,
generally denouncing the hatefulness of speech). Furthermore, machine-generated
counterspeech often employs strategies that humans deem less convincing
compared to human-produced counterspeech. Our findings point to the importance
of accounting for the underlying stereotypical implications of speech when
generating counterspeech and for better machine reasoning about
anti-stereotypical examples.