Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation (2023)

Abstract: Counterspeech has been demonstrated to be an efficacious approach for combating hate speech. While various conventional and controlled approaches have been studied in recent years to generate counterspeech, a counterspeech with a certain intent may not be sufficient in every scenario. Due to the complex and multifaceted nature of hate speech, utilizing multiple forms of counter-narratives with varying intents may be advantageous in different circumstances. In this paper, we explore intent-conditioned counterspeech generation. At first, we develop IntentCONAN, a diversified intent-specific counterspeech dataset with 6831 counterspeeches conditioned on five intents, i.e., informative, denouncing, question, positive, and humour. Subsequently, we propose QUARC, a two-stage framework for intent-conditioned counterspeech generation. QUARC leverages vector-quantized representations learned for each intent category along with PerFuMe, a novel fusion module to incorporate intent-specific information into the model. Our evaluation demonstrates that QUARC outperforms several baselines by an average of ~10% across evaluation metrics. An extensive human evaluation supplements our hypothesis of better and more appropriate responses than comparative systems.

## introduction the quantity and accessibility of information on the internet are constantly growing in the 21st century. this has made it increasingly simpler for users on social media to post hateful or attacking speech, all while hiding behind the veil of anonymity @xcite . hate speech @xcite is an offensive dialogue that uses stereotypes to communicate a hateful ideology, and it can target several protected cs: how can you deny the deaths of more than 6 million jews? cs: historians agree that the concentration camps were a great example of a systematic persecution of jews by the nazi regime occurred. cs: it is a myth perpetuated by anti-semites to justify their hatred of jews. ## dataset we begin by analyzing existing works to determine the intent categories for intentconan. conan @xcite derives nine intent categories from @xcite , whereas @xcite defined seven intent categories with minor variation. in contrast, due to the scarcity of data points, we club a few semantically-similar intents together in intentconan, e.g., we combine the positive and affiliation intents as positive. in total, we consider five intent categories, i.e., informative, question, denouncing, humor, and positive in this work. the publicly-available multi-target conan dataset @xcite consists of ~5, 000 hs-cs pairs. however, it does not comprise any intent label for the counterspeeches. first, we extract the hs-cs pairs and clean them to remove redundancy. subsequently, we employ three do2 . annotation guidelines: prior to the annotation, we make sure that the annotators have a comprehensive understanding of the field-manual 4 for "responding to online abuse". in our pilot study, we conduct several rounds of deliberation with all annotators over the understanding of the counterspeech. in particular, annotators consider the following objectives for every intent of speech: estab-lish the goal: each type of counterspeech necessitates a distinct fundamental idea, speech style, and goal. de-escalate: each counterspeech instance should be written in a manner that would neutralize the situation and, ideally, not provoke retaliation or further hate speech. avoid hostile language: under no circumstance was threatening speech, name-calling, profanity, or hostility to be displayed while annotating counterspeech instances. subsequently, annotators label and write the intentspecific counterspeeches for 3, 583 distinct hate speech instances. ## proposed methodology in this section, we define the architecture and the structural details of our proposed framework, quarc. our key insight is that a counterspeech instance can be decomposed into two distinct components -its semantics and intent. in particular, we can convey the semantics of the same counterspeech (which can be regarded as the compositional message) in multiple manners, such as through humor, as a question, in an informative manner, etc., depending upon the desired intent. more formally, given the counterspeech y i , the semantics s i and the intent c i , we posit that there exists a function ζ such that y i admits a factorization y i ∼ ζ(y i |s i , c i ). the primary goal of our method is to learn contextuallyrich representations to seamlessly integrate the desired intent information with the semantics of the counterspeech to yield effective intent-conditioned counterspeeches. to this end, we design a novel two-phase training pipeline in which we attempt to learn the vector-quantized representations of each intent and propose a fusion mechanism, perfume, to integrate this information into the model. let us denote the dataset d for cogent, we utilize the intent codebook c, assimilated through clime to learn χ, which takes as input the semantic encoding of the hate speech x s i , as well as the encoding of desired intent e f i , to yield ỹi ∼ χ(•|x s i , e f i ). the overall architecture is depicted in figure 2 . ## experimental setup and results in this section, we delineate an exhaustive analysis of our model's performance and also carry out a predictive comparison against text generation models using both human and automatic evaluation. comparative systems: • generate prune select (gps) @xcite uses a three-stage pipeline for generating counterspeeches. the first stage generates a large number of counterspeeches using an autoencoder architecture which is further pruned using a grammatical model. finally, the most suitable counterspeeches are chosen for hate speech using a vector-based response selection model. evaluation metrics: we employ rouge @xcite and meteor @xcite scores to evaluate the syntactic correctness of the generated counterspeech. given that rouge and meteor metrics primarily assess surface-level overlap, their standalone usage may not provide a comprehensive evaluation of the effectiveness of the generated counterspeech instances, considering the possibility of multiple correct outputs. to address this limitation, we augment these metrics by incorporating measures of semantic richness and conducting thorough human evaluations to ensure a more comprehensive assessment. for semantic richness, we report bertscore (bs) @xcite along with cosine similarity (ss) obtained from a sentence-transformers model (all-minilm-v2) @xcite . moreover, to check the efficacy of the models in incorporating the desired intent in the generated counterspeeches, we compute category accuracy (ca) through an intent classification (ic) model. ## conclusion in an effort to address the pervasive issue of hateful speech on the internet, we proposed the novel task of intent-conditioned counterspeech generation. we developed intentconan, the first intent-specific dataset for diverse counterspeech generation. further, to benchmark the dataset, we proposed a novel framework (quarc) that decomposes the task into two phases -clime learns the intent distribution which is subsequently leveraged by cogent to generate the intent-conditioned counterspeeches. we conducted an extensive evaluation (i.e., empirical, qualitative, and human) to establish the effectiveness of quarc.