  Ad hominem attacks are those that target some feature of a person's character
instead of the position the person is maintaining. These attacks are harmful
because they propagate implicit biases and diminish a person's credibility.
Since dialogue systems respond directly to user input, it is important to study
ad hominems in dialogue responses. To this end, we propose categories of ad
hominems, compose an annotated dataset, and build a classifier to analyze human
and dialogue system responses to English Twitter posts. We specifically compare
responses to Twitter topics about marginalized communities (#BlackLivesMatter,
#MeToo) versus other topics (#Vegan, #WFH), because the abusive language of ad
hominems could further amplify the skew of power away from marginalized
populations. Furthermore, we propose a constrained decoding technique that uses
salient $n$-gram similarity as a soft constraint for top-$k$ sampling to reduce
the amount of ad hominems generated. Our results indicate that 1) responses
from both humans and DialoGPT contain more ad hominems for discussions around
marginalized communities, 2) different quantities of ad hominems in the
training data can influence the likelihood of generating ad hominems, and 3) we
can use constrained decoding techniques to reduce ad hominems in generated
dialogue responses.
