“Nice Try, Kiddo”: Investigating Ad Hominems in Dialogue Responses (2021)

Abstract: 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.

## introduction ad hominems attack an opponent's character or identity instead of the points the opponent is making, and can exist in any conversational setting between two or more entities. from an argumentation perspective, ad hominems are fallacies, and fallacies rely on faulty reasoning to advance a point @xcite . these ad hominem fallacies are related to abusive language, toxicity, and microaggressions, and can be expressed with both subtle and explicitly offensive language. our work is motivated by this fallacy's potential to amplify the spread of harmful societal biases. for communities that are already disproportionately harmed by societal power inequalities, ad hominems further amplify the power imbalance. tone policing is a type of ad hominem that seeks to regulate the emotions that a person (usually of a marginalized population) can use to deliver their points (e.g., not too angrily), thereby altogether invalidating the style of delivery, the person's competence, and the points being conveyed. besides directly experiencing ad hominem attacks, marginalized groups could also be disproportionately discouraged from using technologies that propagate these attacks, since abusive language from a technology can deter people from using the technology @xcite . the goal of this study is to analyze ad hominems in dialogue system-and human-generated responses for topics that vary in impact to marginalized populations. through analysis, we formulate techniques to reduce ad hominem responses and thus the associated harms, which is especially important for dialogue systems since these systems directly interact with users. we analyze responses from dialogpt @xcite and humans to english twitter posts. specifically, we compare responses to twitter topics about marginalized communities (#black-livesmatter, #metoo) versus other topics (#vegan, #wfh). through human annotation and trained classifiers, we find that ad hominems exist in both human and dialogpt responses. across response sources, there are more ad hominems in #black-livesmatterand #metoo-related responses, fewer in #vegan-related responses, and even fewer in #wfh-related responses. the presence of more ad hominems in responses to social issues that concern marginalized groups has troubling implications about the amplified harms toward these groups. given our analysis, we further propose a constrained decoding algorithm to reduce the amount of ad hominems generated by dialogue systems. by using salient n-gram similarity to apply soft constraints to top-k sampling, our proposed technique is simple, extensible to reducing other harms, and does not require much additional computation. at each decoding time step, the technique compares the similarity between the current generated output and salient ad hominem versus non-ad hominem n-grams, possibly selecting alternative token candidates to generate. this technique is effective at reducing the amount of ad hominems generated across topics while maintaining coherence and relevance. our main contribution is a novel analysis of ad hominem responses generated by humans and di-alogpt across topics varying in impact to marginalized communities. for this analysis, we propose empirically-derived ad hominem categories that are further verified through annotation. furthermore, we build a new dataset of twitter posts paired with human-and dialogpt-generated responses, where the responses have ad hominem-related labels. finally, we devise a constrained decoding technique that uses salient n-gram similarity to steer top-k sampling away from ad hominem responses. we release data and code at https://github.com/ ewsheng/ad-hom-in-dialogue . ## related work this work is related to a broad spectrum of topics, including prior definitions of ad hominems and how ad hominems facilitate biases. also, analyzing ad hominems in dialogue systems is related to examining offensive language and other harms. lastly, we discuss existing constrained decoding methods. ## dataset and model setup this section describes the dataset collection process and the dialogue model variations we analyze. dataset collection our goal is to understand how ad hominem responses differ across discussions that vary in impact and relevance to marginalized groups. to that end, we extract english [post, response] pairs on different topics from twitter and also use dialogpt to generate responses for all collected posts. we refer to this collective dataset as the adhomintweets dataset. relevant topics are divided into polarizing (i.e., topic polarizing topic affects marginalized group # [post, human resp] pairs blm yes yes 4,037 metoo yes yes 2,859 vegan yes no 3,697 wfh no no 3,992 total --14,585 for the latter, we choose the topic vegan and collect posts that include any of the hashtags: #vegan, #veganism, #govegan, or #veganlife. foot_0 for polarizing topics that are directly relevant to marginalized groups, we focus on the topics blm (from #blacklivesmatter posts) and metoo (from #metoo posts). #blacklivesmatter is related to the "justice, healing, and freedom to black people across the globe", foot_1 and #metoo is related to the movement against sexual violence. 3 in total, we collect 14,585 @xcite pairs of tweets posted between aug. 7 and oct. 29, 2020; detailed data statistics are in models in this work, we analyze responses from the dialogpt (zhang et al., 2020a) dialogue model. dialogpt was originally trained on web data, and then was further fine-tuned for multi-turn conversational capabilities on reddit data. since models can vary in harm depending on the training data, we compare responses from the original medium-sized dialogpt to responses from dialogpt separately fine-tuned on each of the four topics from the human response subset of adhomintweets. 4 4 identifying ad hominem responses it is generally difficult to settle on a comprehensive list of ad hominem categories. we build ah type topic post response stupidity blm together. #blacklivesmatter that's a dumb thing to say. ignorance blm your all welcome to join in on the #blm movement! you mean "you're" trolling/lying vegan it's time to end intensive meat production...#vegan you must be a troll. bias blm this is why people are protesting, this is why the #blm movement is necessary. you're racist because you focus on race. condescension metoo 3 years into #metoo era, real apologies are few and far between can you stay out of grown folks' business... other vegan it's not a 'personal choice' when a 'victim' is involved. #govegan you're better than this. non-ah wfh #wfh benefit: no co-worker judgement microwaving fish for lunch the smell of fish is deadly. ## conclusion ad hominem responses from dialogue systems are offensive, stall conversations, and are especially harmful for marginalized communities. we analyze responses to find that discussions on topics that affect marginalized groups contain more ad hominems. through a novel constrained decoding technique, we decrease the amount of ad hominems generated from dialogue systems while keeping the response quality comparable. furthermore, our method can be easily applied to other pre-trained language generation models and other subtle yet harmful language. more broadly, our work strives to understand ad hominems in the context of harms in conversational systems.