Fair and Argumentative Language Modeling for Computational Argumentation (2022)

Abstract: Although much work in NLP has focused on measuring and mitigating stereotypical bias in semantic spaces, research addressing bias in computational argumentation is still in its infancy. In this paper, we address this research gap and conduct a thorough investigation of bias in argumentative language models. To this end, we introduce ABBA, a novel resource for bias measurement specifically tailored to argumentation. We employ our resource to assess the effect of argumentative fine-tuning and debiasing on the intrinsic bias found in transformer-based language models using a lightweight adapter-based approach that is more sustainable and parameter-efficient than full fine-tuning. Finally, we analyze the potential impact of language model debiasing on the performance in argument quality prediction, a downstream task of computational argumentation. Our results show that we are able to successfully and sustainably remove bias in general and argumentative language models while preserving (and sometimes improving) model performance in downstream tasks. We make all experimental code and data available at https://github.com/umanlp/FairArgumentativeLM .

## introduction recently, pre-trained language models (plms), e.g., bert @xcite , roberta @xcite , gpt-2 @xcite and dialogpt @xcite have been shown to encode and amplify a range of stereotypical biases, such as racism, and sexism (e.g., @xcite @xcite . while such types of biases provide the basis for interesting academic research, e.g., historical analyses (e.g., @xcite @xcite , stereotyping constitutes a representational harm @xcite , and can lead in many concrete socio-technical application scenarios to severe ethical issues by reinforcing societal biases @xcite @xcite . but while prior work has focused on how to evaluate and mitigate unfair biases for general-purpose lms (e.g., @xcite and their applications to specific domains and genre like, for instance, conversational lms (e.g., @xcite , there has been little attention to the problem of bias in argumentative language. this is despite previous work from @xcite pointing out the high potential for harm, due to the high sensitivity of envisioned applications like self-determined opinion formation systems, as well as, crucially, showing that argumentative corpora like those from the online debate portal debate.org @xcite do encode unfair biases, which are likely to be captured by argumentative lms. this is particularly problematic as research in computational argumentation regularly makes use of such corpora for injecting knowledge about argumentative language into plms (e.g., @xcite . still, to date, there is neither an evaluation resource specifically tailored to argumentative language, nor knowledge on debiasing argumentative lms or on the effects of debiasing on argumentative downstream tasks. ## ab ba : a new annotated corpus of bias in argumentative text we create ab ba , the first annotated corpus of bias in argumentative text following the methodology from @xcite : (1) specification of the social biases of interest, (2) retrieval of candidates of biased statements, and (3) manual annotation. ## adapter-based fair argumentative language models to obtain a fair and argumentative lm, we conduct both argumentative and debiasing language dimension example sentence label islamophobia 6 billion muslims around the world are following the religion of violence, hate and terror. biased i would agree that there should be punishments for terrorism, but not for islam itself. unbiased queerphobia thus, since being gay is a sin and sins are poor choices, being gay is a choice. biased the stigma of homosexuals being more promiscuous is a horrible lie. argumentation adapter. following @xcite , we tune general pre-trained models on a large set of arguments to obtain an argumentative language model. in contrast to the original work, we rely on language adapters. concretely, we adopt the architecture proposed by @xcite , which inserts a single adapter, a two-layer feedforward network, into each transformer layer. the output of the adapter is computed as with the two matrices @xmath0, respectively, h as the transformer's hidden state, and r as the residual. in addition, we inject invertible adapters, which are stacked on top of the embedding layer and the inverses of the invertible adapters are placed in front of the output layer. they perform a similar function to the language adapters, but aim to capture token-level specific transformations @xcite . both the language adapters and the invertible adapters are trained on a language modeling task using a causal language modeling loss for auto-regressive models and a masked language modeling loss for autoencoding models, respectively. debiasing adapter. for debiasing, we inject debiasing adapters @xcite into the models, using the same adapter architecture as before. following the original work, we use counterfactual data augmentation @xcite and train the adapter parameters on the augmented corpus to break stereotypical associations in the model. to this end, we manually compile pairs of opposing target terms (t i , t j ) ∈ t 1 × t 2 , such that t i forms the most suitable antonym of t j in the sense of minority and dominant group (e.g., muslim and christian) and can be substituted grammatically interchangeably. while this is arguably straightforward with the islamophobia bias specifications, the target terms of the queerness dimension are more complex to juxtapose. therefore, we clustered them into three groups of 'sexual identity' (e.g., {gay, straight}), 'gender identity' (e.g., {transgender, cisgender}) and 'biological sex' (e.g., {androgyne, unisexual}) so as to find the best matching pairs of antonyms (cf. the list in the appendix). we then replace all occurring target terms from t 1 or t 2 with their opposite term from the set of tuples @xmath1, t j )} n (we randomly select a term from the list if multiple substitutions are possible). we opt for a two-sided application of cda, keeping both the counterfactual and the original sentences in the training set to avoid over-correction @xcite . we append each counterfactual sentence immediately after its original counterpart and train in two settings, namely using: a) only biased and counterfactual sentences; b) all sentences, i.e., also including neutral ones. combining adapters. we investigate three different architectures: first, in §4.3, we study two architectures using adapterstacking @xcite , i.e., by stacking the argumentation adapter on top of a debiasing adapter and vice versa (figure 1 ). second, in §4.4, we compare the best architectures from §4.3 with adapterfusion @xcite , which requires training additional network layers for interpolating the adapters' outputs. ## experiments and results we next describe the experiments to answer the research questions rq1 through rq4 (section 1) that underpin our investigation. ## conclusion in this work, we presented an investigation of bias in plms and argumentative text. to this end, we created ab ba , the first annotated corpus tailored for measuring bias in computational argumentation models. using ab ba , we showed that argumentative fine-tuning of language models may lead to an amplification of biases in the models. we then demonstrated how to obtain a fair and argumentative language model by combining argumentation with debiasing knowledge encapsulated in lightweight adapters to ensure higher sustainability and flexibility, and analyzed the effect of stacking orders. an additional downstream evaluation on argument quality prediction indicated that debiasing can even lead in some cases to improved results. we hope that with this work, especially the novel ab ba resource, we will foster further research on fair computational argumentation.