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 ( #Black-LivesMatter , #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 topk 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. 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 (Hansen, 2020). These ad hominem fallacies are related to abusive language, toxicity, and microaggressions, and can be expressed with both subtle and explicitly offensive language. Table 1 presents Post: Many are trying to co-opt and mischaracterize the #blacklivesmatter movement. examples of ad hominem responses to Twitter posts. Undesirable in any response, ad hominems are unproductive in furthering a meaningful discussion and can reinforce falsehoods. However, these attacks appeal to emotions and implicit biases to argue a point, and are thus often effectively harmful regardless of whether the attacks are true, recognized, or retracted (Yap, 2013). 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 (Sood et al., 2012b). The goal of this study is to analyze ad hominems in dialogue systemand 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 (Zhang et al., 2020a) 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-LivesMatter and #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 topk 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 DialoGPT 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 humanand 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 topk sampling away from ad hominem responses. We release data and code at https://github.com/ ewsheng/ad-hom-in-dialogue . 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. Ad Hominems In the argumentation literature, theoretical ad hominems include the abusive (attack on the opponent's character), tu quoque (he did it first), circumstantial (accusation of hypocrisy), and guilt by association (associating the opponent with someone with low credibility) (Walton, 1998; Woods, 2007). Wijze (2003) criticizes that these textbook examples are not realistic in conversation. For more empirical categories, Habernal et al. (2018) propose ad hominem types based on analysis of Reddit's ChangeMyView discussion threads, and Delobelle et al. (2019) analyze the name-calling and abusive categories. Moreover, Wulczyn et al. (2017) use classifiers for a large-scale analysis of personal attacks in Wikipedia comments. We build upon prior works to define and analyze ad hominems in a conversational setting. Additionally, Yap (2013) discusses the harmful effects of implicit biases in forming and evaluating ad hominems. They emphasize that ad hominem attacks can be harmful to a person's credibility and expertise even if the attack is recognized as fallacious and irrelevant to the argument. In particular, because societal norms allow biases and stereotypes to detract from a person's credibility or expertise, the use of ad hominems can further diminish the rhetorical credibility (Govier, 1993) of marginalized groups. Offensive Language Detection Ad hominems occur in many forms and are related to different types of offensive language, including abusive language (Yin et al., 2009; Chen et al., 2012; Nobata et al., 2016), hate speech (Warner and Hirschberg, 2012; Kwok and Wang, 2013; Djuric et al., 2015), profanity (Sood et al., 2012a), and the more subtle forms of microaggressions (Breitfeller et al., 2019) and projecting biases and stereotypes through power differentials in language (Sap et al., 2020). Ranging from outright insults to condescension, ad hominems are a form of offensive language that is difficult to comprehensively and objectively define. Nonetheless, these responses are important to characterize, since they can irreparably damage a person's credibility. It is also generally important to identify these subtle forms of offensive language, since it is unclear if existing offensive language detection techniques are equally effective for these subtle forms. Harms in Dialogue Systems Conversational systems are known to perpetuate several types of harms. Ruane et al. (2019) caution about harms that can result from using conversational systems and propose striving for trust and transparency; Roller et al. (2020) suggest techniques for chatbot safety. For analysis, Sheng et al. (2019) evaluate societal biases in language generation, Curry and Rieser (2018) study how conversational systems respond to sexual harassment, and Khatri et al. (2018) detect offensive content with a semi-supervised approach. To reduce harms, Sheng et al. (2020) present a framework for controlling biases in language generation, and Dinan et al. (2019) show how adversarial attacks can make models more robust to offensive language usage from humans. Constrained Decoding For constrained decoding, prior works focus on incorporating words or phrases (as hard or soft constraints) into the decoded output. Swanson et al. (2014) and Balakr-ishnan et al. (2019) use parse trees among other techniques to enforce constraints in the generated text. Hokamp and Liu (2017); Post and Vilar (2018) propose variants of Grid Beam Search, which generate output that include lexical constraints. Miao et al. (2019); Zhang et al. (2020b); Susanto et al. (2020) explore insertion-based non-autoregressive decoding algorithms. To be compatible with an autoregressive model like DialoGPT and effective for open-domain generation, we apply constrained decoding to topk sampling. Our method also differs from these prior works in that it imposes soft constraints to not generate phrases that are likely to lead to ad hominems. Decoding-time techniques that can be used to reduce harmful language generation, e.g., the Plug and Play Language Model (PPLM) (Dathathri et al., 2020), are most relevant to our technique. 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. controversial) and non-polarizing; we expect there to be more strong opinions for the polarizing topics and thus more ad hominem responses for those topics. For this study, we choose the topic WFH (work from home) as a non-polarizing topic and collect Twitter posts that include the hashtag #wfh or #workingfromhome . Polarizing topics can further be divided into those that are directly relevant to marginalized communities and those that are not. For the latter, we choose the topic Vegan and collect posts that include any of the hashtags: #vegan , #veganism , #govegan , or #veganlife . 1 For polarizing topics that are directly relevant to marginalized groups, we focus on the topics BLM (from #black-livesmatter posts) and MeToo (from #metoo posts). #blacklivesmatter is related to the justice, healing, and freedom to Black people across the globe, 2 and #metoo is related to the movement against sexual violence. 3 In total, we collect 14,585 [post, response] pairs of Tweets posted between Aug. 7 and Oct. 29, 2020; detailed data statistics are in Table 2. We replace all usernames and urls with special placeholders to better anonymize the data. 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 1 Habernal et al. (2018) find that vegan-related topics are one of the top topics that contain ad hominems in their study. 2 https://blacklivesmatter.com 3 https://metoomvmt.org 4 More details are in Appendix A.2. upon the work of Habernal et al. (2018) to devise ad hominem categories that are both empirically-motivated and can be annotated with high inter-annotator agreement. We specifically include categories such as ignorance and condescension to cover more subtle forms of personal attacks (e.g., tone policing, mansplaining) that could further diminish the credibility of those who are already marginalized. We also limit the definition of ad hominem to personal attacks towards the author of the post and not a third person. We collect human annotations that can then be used for analysis and training a classifier to automatically label ad hominems. Although Habernal et al. (2018) propose a similar typology of ad hominems, there is no existing dataset annotated with their empirically-derived categories. Moreover, we study ad hominems in casual conversational settings. For these reasons, we annotate a subset of ADHOMINTWEETS with ad hominem information. To measure inter-annotator agreement, we calculate the Worker Agreement With Aggregate (WAWA) score, following Ning et al. (2020). The WAWA score compares the majority votes against each annotator and micro-averages the resulting precision, recall, and F 1 scores. 5 Heuristics for Ad Hominems Ad hominem responses are relatively rare and range broadly from explicit to more subtle forms. For more effective annotation, we use heuristics to choose [post, response] pairs where the response is likely to be an ad hominem. In preliminary analyses, we find that responses that contain certain  you -phrases such 5 There are also other agreement metrics such as Krippen-dorff's alpha, but because we expect our data to have many more non-ad hominem compared to ad hominem responses, alpha scores can be misleadingthe WAWA score gives a more appropriate estimate of annotator agreement. as  you are  are more likely to have ad hominems. We call these responses you-responses . 6 In addition to pairs with you-responses , we also collect random pairs without you-responses for annotation to ensure that the annotated samples are representative of different ad hominems. Annotation Task We ask annotators on Mechanical Turk to read a post and response and determine whether the response contains any ad hominem(s) towards the person who made the post. We divide ad hominems into the following categories: stupidity , ignorance , trolling/lying , bias , condescension , and other ; examples are in Table 3. 7 Annotation Round 1 The goal for the first round of human annotation is to collect enough data to train an ad hominem classifier. To balance targeted and random samples, for each topic ( BLM , MeToo , Vegan , WFH ) and response source (human, DialoGPT) pair, we randomly select 150 [post, response] pairs with you-responses and another 150 pairs without you-responses for annotation. In total, we gather 2,400 [post, response] pairs that are then annotated through Mechanical Turk. Additional Annotations We conduct three more rounds of annotations to retrieve more ad hominem responses. For the second and third rounds, we use an ad hominem classifier trained on data from all previous rounds (with the same architecture and hyperparameters as the final classifier in Sec. 4.2) to label unseen samples in ADHOMINTWEETS . We then select a balanced amount of automatically-labeled ad hominems and non-ad hominems from each [topic, response source] pair to annotate. 8 Some topics (e.g., WFH and Vegan ) prompt fewer ad hominem responses, so it is difficult to 6 Full set of you-responses is in Appendix A.1. find enough of these responses in the wild to train a more accurate classifier. Our solution is to manually take the responses annotated as ad hominems and pair them with WFH or Vegan posts. To verify that these new pairs contain ad hominem responses, we run a fourth round of annotation on these pairs and only keep the ones where the majority of annotators label the response as an ad hominem to the post. We combine majority annotations across all rounds of annotations to train the final ad hominem classifier used for analysis. For large-scale analysis of ad hominems in human and dialogue system responses, we rely on classifier annotation. To simplify the learning problem, we condense the different ad hominem categories into a binary yes/no scheme, where yes" indicates the presence of any type and quantity of ad hominems in the response given the post. We build a classifier to automatically label whether a response contains ad hominems for a given post by fine-tuning a BERT (Devlin et al., 2019) model with the input format [CLS] POST [SEP] RESPONSE [SEP]. We additionally include comparisons to a baseline classifier built on top of DialoGPT to similarly label whether a post and response pair indicates the presence of an ad hominem response. This baseline classifier allows a comparative evaluation of a bi-directional encoder model versus an auto-regressive decoder model for ad hominem classification and how this difference may affect the quality of control techniques that rely on the latter (e.g., PPLM (Dathathri et al., 2020), GeDi (Krause et al., 2020)). Appendix A.2 includes more details of our model implementation and data statistics (Table 8). Ultimately, the goal is to train an ad hominem detection classifier that has high accuracy across sources and topics, so we curate the dev and test datasets to be balanced across topics, response sources, and ad hominem versus non-ad hominem samples (through downsampling). Because of the natural imbalance of ad hominem responses for different topics, ad hominem responses for topics like WFH are relatively sparse compared to those for topics like BLM . We automatically augment our training set to combat this sparsity. First, we accumulate all posts and responses not present in the dev and test sets. Next, we choose a random post to pair with a random labeled response to form a new sample. We generate these new data samples to roughly balance the number of samples across topics and across ad hominems versus non-ad hominems for each topic. These new combinations of [post, response] pairs help de-emphasize spurious correlations between topics and classifier labels. Since the automatic augmentation reduces emphasis on the post when predicting the presence of ad hominems in the response, a natural question is if the post is really necessary to gauge whether the response contains ad hominems. The answer is mixedfor example, the response  you're a troll  is an ad hominem for any post. However, the response  those who promote veganism are arrogant fools  is an ad hominem given the post  everyone should follow veganism , but not an ad hominem given the post  I don't understand veganism . Empirically, by limiting the classifier input to only responses, the classifier performs worse than if it has both the post and response as input. 9 5 Reducing Ad Hominem Responses Inspired by the success of n -gram features in detecting abusive language by Nobata et al. (2016), we propose a constrained decoding algorithm to discourage the model from generating n -grams that are semantically similar to salient n -grams found in ad hominem responses. While we motivate this technique within the context of ad hominems, the technique is applicable to other subtle harms (e.g., microaggressions) in language generation. A naive method to generate fewer ad hominems is to block words that are likely to occur in ad hominems. However, ad hominems are contextually determined, meaning that phrases are a better indicator than words, thus motivating our use of n -grams. Additionally, our algorithm uses soft constraints because there are no words or phrases that always indicate the presence of an ad hominem. In this section, we describe how our technique SALIENSIMTOPk extends topk sampling by incorporating n -gram similarity constraints. Salient n -grams We define salient ad hominem n -grams to be n -grams that appear more frequently in ad hominem responses than in non-ad hominem responses. Similarly, salient non-ad hominem n 9 By randomly forming new (post, response) pairs during augmentation, we do not explicitly account for the responses that are context-specific; however, we find the context-specific responses to be relatively rare and that our augmentation empirically results in a more robust classifier. grams appear more frequently in non-ad hominem responses than in ad hominem responses. We use the salience score as defined by Li et al. (2018): S ( u, a ) = count( u, D a ) +  (cid:16)(cid:80) a (cid:48) A ,a (cid:48) (cid:54) = a count( u, D a (cid:48) ) (cid:17) + . (1) In Eq. (1), u denotes an n -gram, D = { ( s 1 , a 1 ) , ..., ( s m , a m ) } is a corpus where each sample is a sentence s i labeled with attribute a i . D a is therefore the set of sentences in the corpus with the same attribute a . A is the set of possible attributes (e.g., ad hominem or non-ad hominem). We define the n -gram u to be salient for the attribute a if S ( u, a )   . We find setting the smoothing parameter  = 0 . 5 and threshold  = 5 . 5 effective for our experiments, and we compute the salience of 3 -, 4 -, and 5 -grams. Table 4 shows that the top salient ad hominem n -grams are intuitively those that are likely to lead to ad hominems. For example,  you're being a  is used in contexts such as  you're being a hypocrite . A more overt example of a phrase likely to lead to an ad hominem response is  you're a troll . The amount of you-responses in salient ad hominem n grams verify our intuition that many ad hominem responses occur in the form of you-responses . Also, we find that there are more salient ad hominem n grams than non-ad hominem n -grams, and that the former generally have higher salience scores. These observations and preliminary experiments suggested that it is useful to consider both types of salient n -grams to reduce ad hominems. Topk Sampling For open domain language generation, topk sampling (Fan et al., 2018) and topp nucleus sampling (Holtzman et al., 2019) are popular decoding algorithms that have been shown to maintain topic consistency and promote diversity. We experiment with constrained decoding through topk sampling, though our technique is also applicable to nucleus sampling. As topk sampling is a general decoding algorithm that can be used with Algorithm 1: SALIENSIMTOPk Data: input tokens x , # top tokens k , # candidate tokens t , # recent tokens r , salient ad hominem average n -grams A , salient non-ad hominem average n -grams B , semantic similarity threshold  Result: output tokens y y = x while len( y ) < max_steps + len( x ) do vocab_logits = model( y ) P (cid:48) = choose topk vocab_logits and rescale candidate_tokens = sample t tokens using P (cid:48) for cand in candidate_tokens do if special_condition then y various language generation models without further tuning or training, expanding upon this technique allows for a computationally-light generalizability. SALIENSIMTOPk We reduce the amount of generated ad hominems by encouraging the generation of n -grams that are semantically dissimilar to salient ad hominem n -grams and similar to salient non-ad hominem n -grams. Alg. 1 details constraints we add to topk sampling. In the for-loop, we iterate through each candidate token. If the current generated output meets a special_condition (e.g., backtracking limit, first r time steps), then we select the current candidate token. Otherwise we retrieve and average DialoGPT's embeddings over the most recently generated r -gram to calculate c , an e -dimensional vector where e is the size of the token embedding. We similarly compute representations to form A , a j  e matrix of j salient ad hominem average n -gram embeddings, and B , a k  e matrix of k salient non-ad hominem average n -gram embeddings. We then calculate the average pairwise similarity sim_a = 1 j (cid:80) ji =1 sim ( A i , c ) , where A i is the i -th row of A , and similarly for sim_b. We select the current token if the difference between the similarities is under a threshold  , i.e., the current r -gram is less similar to the ad hominem n -grams and more similar to the non-ad hominem n -grams. Otherwise, we backtrack to the previous time step if we iterate through all candidates without finding a suitable one. By limiting the number of times the algorithm can backtrack while gen-Topic Source dev test avg BLM Human 83.3 82.9 83.1 DialoGPT 84.2 75.7 80.0 MeToo Human 80.0 73.7 76.9 DialoGPT 85.0 80.0 82.5 Vegan Human 80.0 70.6 75.3 DialoGPT 82.9 82.9 82.9 WFH Human 77.8 83.3 80.6 DialoGPT 92.3 88.4 90.4 Table 5: BERT-based classifier F 1 scores for ad hominem responses across topics and response sources. erating a sample, this algorithm adds a constant amount of computational resources compared to the original, non-constrained decoding. Implementation Details In our experiments, we set k = 40 (commonly used in previous generation tasks (Radford et al., 2019)). With parameter tuning, we find t = 10 and  = 0 effective for our setup. We use r = 5 to compare the averaged embedding of the most recent 5 -gram with those of salient 3 -, 4 -, and 5 -grams. Additionally, we use cosine similarity as the similarity metric and our special_condition includes either a) a limit of 5 for backtracking or b) the first r time steps. Annotation Across all rounds of annotations, the average WAWA scores include a precision of 0.82, recall of 0.92, and F 1 of 0.87, indicating moderately high majority agreement. Generally, the agreement scores for the human responses are slightly higher than those for the DialoGPT responseswe hypothesize that the former tend to be more coherent and longer, and thus more informative. Ad Hominem Classifier The resulting BERT-based classifier has an overall dev F 1 score of 83.3% and a test F 1 score of 80.0% for ad hominems. The DialoGPT-based classifier has a dev F 1 score of 74.6% and a test F 1 score of 72.6%, supporting our use of the BERT-based classifier to automatically detect ad hominems in the rest of this work. 10 The full breakdown of F 1 scores across topics and response sources is shown in Table 5 and Appendix Table 9. Ad Hominem Categories By comparing ad hominem types across the manually-annotated human and DialoGPT responses, we find that ad hominems in human responses frequently occur in the forms of condescension and ignorance, while ad hominems in DialoGPT responses occur in the forms of ignorance and other types (Ta-ble 11 in the Appendix). These results indicate that responses from different sources and topics are likely to contain different ad hominems. Formally categorizing ad hominems allows for more consistent annotations and a better understanding of the types DialoGPT is prone to generate. DialoGPT Responses The classifier enables us to perform a large-scale study of ad hominem trends across various contexts for the entire ADHOMINTWEETS dataset. Figure 1 shows the percentage of ad hominem responses to posts across topics and response sources. Focusing on the Hu-man and DialoGPT bars for each topic, we see that ad hominem responses are present across all topics for both response sources. Additionally, ad hominem responses occur more frequently in discussions related to BLM and MeToo and less frequently in discussions related to Vegan and WFH . Vegan discussions also seem to attract more ad hominem responses than WFH discussions. The relatively higher rates of ad hominem responses in topics related to marginalized communities indicate the elevated potential for harm towards these communities. Fine-tuned DialoGPT Responses Figure 1 also shows that fine-tuning on datasets that contain more ad hominem responses leads to more generation of ad hominem responses across topics. 11 From these results, we infer that the original DialoGPT (which was fine-tuned from GPT-2) was trained on a dataset that likely contained relatively more rather than fewer ad hominems. Additionally, fine-tuning on a carefully chosen dataset can reduce the quantity of generated ad hominems and associated harms. Baselines We compare techniques from two classes of harm reduction methods for language generation: data-based and decoding-based. Gehman et al. (2020) define data-based techniques as those where further model training on more data is necessary and decoding-based techniques as those where the generation strategy is changed without changing model parameters. For our main decoding-based SALIENSIMTOPk technique, we 11 Table 13 in the Appendix includes examples generated by the fine-tuned models. introduce four baselines to span the different classes of harm reduction techniques. The first baseline is simply the original DialoGPT. Our data-based reduction baseline is DialoGPT fine-tuned on the WFH dataset, as described in Sec. 3. For the first decoding-based baseline, we rely on a gradient-based method post-training to find a trig-ger phrase, which is then attached to a prompt at inference time to influence the generated output (Wallace et al., 2019). Sheng et al. (2020) further propose a framework to use these triggers to control societal biases, and we use these methods to find a trigger that can induce DialoGPT to generate fewer ad hominems and more non-ad hominems when prepended to posts about different topics. For the second decoding-based baseline, we use the Plug and Play Language Model (PPLM) proposed by Dathathri et al. (2020), which guides a pre-trained language model's generated output using gradients from attribute classifiers. 12 Human Annotation To verify ad hominem trends from the automatic evaluation, we randomly select 100 samples from each [reduction technique, topic] pair for additional human annotation. General Trends Classifier and human evaluations for techniques to reduce ad hominems are in Figure 2, and examples of generated responses are in Table 6. The classifier-labeled results allow us to evaluate 14.5K samples across all topics per response source, and the human-labeled results allow us to more accurately evaluate a smaller set of samples. Overall, the trends for classifier and human evaluations are similar, and the evaluations suggest that all ad hominem reduction techniques are effective compared to the original DialoGPT. Furthermore, SALIENSIMTOPk is more effective than the other individual techniques, and combining fine-tuning and SALIENSIMTOPk has promise for further reducing the amount of generated ad 12 More details are in Appendix A.3 and A.4. For SALIENSIMTOPk , limiting the number of times we backtrack to previous time steps ensures that the algorithm is not significantly slower compared to the original topk sampling algorithm. Empirically, we find that using SALIENSIMTOPk with a backtracking limit of 5 on the original DialoGPT results in 13% of the decoding operations being non-forward operations, where the set of decoding operations are: a) choosing the current token and moving forward to the next timestep, b) looking for an alternate token at the same timestep, or c) moving backward to a previous timestep. When applying constrained decoding to DialoGPT fine-tuned on WFH , 10% of the operations are non-forward operations. Since ad hominems are less common than non-ad hominems, the algorithm is able to proceed with the first sampled candidate token in most time steps. Additionally, models or topics that are inclined to generate more ad hominems incur more non-forward operations. Coherence and Relevance Evaluation To ensure that the ad hominem reduction techniques do not affect the quality of the generated responses, we have annotators label the coherence and relevance of a response to a post, both on a scale of 1 to 5, where a higher score is better. The trigger method produces samples that are relatively more coherent, although at the cost of lower relevance to the post. PPLM generates responses that are relatively lower in both coherence and relevance. SALIENSIMTOPk manages to maintain a decent balance of generating both coherent and relevant responses. Combining SALIENSIMTOPk with fine-tuning on WFH data results in responses that are slightly less coherent and mixed in relevance for different topics. coherence (0.38), indicating the task subjectivity. Discussion The collective results indicate that SALIENSIMTOPk is an effective standalone ad hominem reduction technique that maintains generated text quality; while it can be combined with other techniques to further reduce ad hominems, one should carefully evaluate the trade-offs between response coherence and relevance. Additionally, for reducing harmful language types that are more subjective or difficult to detect, straightforward control techniques that rely on salient n grams may be more useful than techniques that rely on noisier signals from classifiers. 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. This work identifies personal attacks in responses generated by dialogue systems, quantifies the dis-13 proportionate amount generated for topics concerning marginalized populations, and proposes methods to reduce ad hominem-related harms. Dataset We collect an English dataset from Twitter and ensure that personal information (e.g., usernames, emails, urls) is discarded. We also collect crowd-sourced annotations for this dataset through Mechanical Turk, where we ask for judgements of whether a response contains ad hominems for a given post, and the coherence and relevance of a response. No information about the annotators are collected from the annotation tasks. The annotation information (pay per amount of work, guidelines) is in the Appendix. One annotation aspect that we did not control for is whether the annotators themselves are from marginalized communities. When measuring harms towards different demographics, it is important to consider the lived experiences of those groups and how these experiences may affect our analyses. Future work includes specifically collecting annotations from marginalized groups. Additionally, we analyze ad hominems in responses to four Twitter topics and from one dialogue model, which leaves much room for exploring the generalizability of the trends we see. Techniques In terms of dual-use harms, our constrained decoding technique could potentially be used to amplify rather than reduce ad hominems (or other harmful language). However, we believe that by being transparent about this technique and releasing the associated code and data, we can better counter attempts of malicious misuse. Furthermore, to perform a large-scale analysis of ad hominems across different contexts, we build an automatic classifier. While we spent much effort on collecting representative train/dev/test datasets and verifying classifier quality and observed trends with human labels, collecting more (diverse) data could help further improve the classifier accuracy and robustness. In the meantime, we think this work introduces an important perspective of how ad hominems in dialogue systems reinforce unequal harms and effective reduction methods. We would like to thank members of the PLUS Lab and the anonymous reviewers for the helpful feedback, and Jason Teoh for the many discussions. This paper is supported in part by NSF IIS 1927554 and by the CwC program under Contract W911NF-15-1-0543 with the US Defense Advanced Research Projects Agency (DARPA). The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government.
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