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