  Hateful comments are prevalent on social media platforms. Although tools for
automatically detecting, flagging, and blocking such false, offensive, and
harmful content online have lately matured, such reactive and brute force
methods alone provide short-term and superficial remedies while the
perpetrators persist. With the public availability of large language models
which can generate articulate synthetic and engaging content at scale, there
are concerns about the rapid growth of dissemination of such malicious content
on the web. There is now a need to focus on deeper, long-term solutions that
involve engaging with the human perpetrator behind the source of the content to
change their viewpoint or at least bring down the rhetoric using persuasive
means. To do that, we propose defining and experimenting with controllable
strategies for generating counter-arguments to hateful comments in online
conversations. We experiment with controlling response generation using
features based on (i) argument structure and reasoning-based Walton argument
schemes, (ii) counter-argument speech acts, and (iii) human
characteristics-based qualities such as Big-5 personality traits and human
values. Using automatic and human evaluations, we determine the best
combination of features that generate fluent, argumentative, and logically
sound arguments for countering hate. We further share the developed
computational models for automatically annotating text with such features, and
a silver-standard annotated version of an existing hate speech dialog corpora.
