To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation (2022)

Abstract: Gender bias may emerge from an unequal representation of agency and power, for example, by portraying women frequently as passive and powerless (“She accepted her future”) and men as proactive and powerful (“He chose his future”). When language models learn from respective texts, they may reproduce or even amplify the bias. An effective way to mitigate bias is to generate counterfactual sentences with opposite agency and power to the training. Recent work targeted agency-specific verbs from a lexicon to this end. We argue that this is insufficient, due to the interaction of agency and power and their dependence on context. In this paper, we thus develop a new rewriting model that identifies verbs with the desired agency and power in the context of the given sentence. The verbs’ probability is then boosted to encourage the model to rewrite both connotations jointly. According to automatic metrics, our model effectively controls for power while being competitive in agency to the state of the art. In our main evaluation, human annotators favored its counterfactuals in terms of both connotations, also deeming its meaning preservation better.

## introduction gender bias refers to the conscious or unconscious unequal treatment of people because of being male, female, or diverse. in natural language text, it manifests in various ways, including the explicit expression of stereotypes and discrimination as well as implicit prejudicial or generalized representations of genders @xcite . language models that learn from such text may reproduce or even amplify the bias @xcite ). an effective approach to mitigate this behavior is to reduce bias in the training data @xcite . in particular, augmenting the data with counterfactuals has been shown to effectively reduce bias in language models @xcite . generating counter-factuals that change the depiction of people through the choice of words is the focus of our research. several works have analyzed gender bias in the subliminal messages transmitted by the framing of people's actions @xcite @xcite . they suggest that the framing of an action influences how the reader perceives the acting person behind. the verb choice can therefore weaken or strengthen the person under consideration @xcite , as in the following example: ## related work unequal communication towards social groups, for example in the form of texts, can be the origin of social bias and is one of the main reasons why individuals, their characteristics, and their actions are not perceived correctly. instead, people's perceptions are often overshadowed and distorted by prejudiced beliefs, resulting in potentially unfair treatment @xcite . different types of social bias have been studied in nlp research recently @xcite @xcite . we focus on one of the most prevailing types, gender bias. for comparability with prior work, we use existing datasets in our experiments, limiting them to binary gender instead of considering further social and linguistic gender categories (cao and @xcite . previous work analyzed implicit forms of gender bias conveyed through language, often reflected by imbalances in connotation frames that capture subjective roles and relations conveyed by a predicate @xcite . connotation frames were introduced by @xcite , who studied the sentiment and presuppositions of predicates. @xcite extended their notion by explicitly modeling agency and power. the authors created a connotation frame lexicon of common verbs, 2146 of which were manually assigned an agency level, 1737 a power level (positive, equal, or negative). they used the lexicon to compare movie characters, finding that males are generally portrayed with more agency and power. @xcite and @xcite found power imbalances in media articles. for example, female politicians are often portrayed as less powerful than their actual role in society compared to males. instead of identifying gender bias, we focus on mitigating it. bias mitigation has been addressed at the preprocessing, the training, and the postprocessing level @xcite . one preprocessing approach is to balance gender occurrences in training data. for example, @xcite and sun et al. (2021) learned on parallel corpora to change the gender of sentences across languages. park et al. (2018) augmented data with gendered sentences to reduce bias in word embeddings, and zmigrod @xcite aimed to convert between masculine and feminine inflected sentences without parallel data. at the training level, @xcite adapted the training process and applied bias controlled training to generative dialogue models to make them generate an equal number of gendered words for both genders considered. lastly, bolukbasi et al. (2016), zhao et al. (2019) and liang et al. (2020) to debias text through the lens of agency connotations, @xcite formalized a new rewriting task called controllable debiasing that seeks to correct implicit bias in textual portrayals. unlike its name (powertransformer) suggests, their approach aims to change the agency connotation of an input sentence (not power). using the agency lexicon of @xcite , @xcite provide information about agency connotations to the model using self-supervision on a reconstruction task and auxiliary supervision on a paraphrasing task. inspired by @xcite , they performed vocabulary boosting at each decoding step based on the agency lexicon to further enhance the agency change. in this paper, we build on the rewriting model of @xcite , but we extend it to jointly control for agency and power. moreover, we substantially refine the rewriting process by using classifiers to determine which verbs to boost in the given context. existing classifiers rely on logistic and kernel ridge regression for agency and power, based on decontextualized elmo @xcite and bert @xcite embeddings of a verb @xcite @xcite . in contrast, we use the whole sentence context as input and perform classification, improving the state of the art in our experiments. ## approach this section presents our approach to generating counterfactuals for gender bias mitigation. based on the contextual classification of verbs, it rewrites sentences jointly in terms of agency and power. overview figure 1 depicts the two parts of the approach: (1) given a sentence, we identify candidate verbs for its rewritten version. to foster meaning preservation, we filter verbs similar to the target connotations positive agency positive power @xmath0. masking encoding agency and power @xmath1. output sentence 2. connotation rewriting 1. candidate verb filtering next-word logits boosted logits similar verbs classified verbs figure 1: proposed approach: after masking the verb in the input sentence, all similar verbs from a lexicon l are classified for agency and power in the sentence context. a transformer then rewrites the sentence. at each decoding step, the unnormalized token probabilities (logits) of verbs with target agency and power are increased. ## data as part of our experiments, we employ data for two purposes: first, to train the agency and power classifiers, we require sentences that include verbs from the given connotation frame lexicon in a variety of contexts. we therefore combine sentences from three corpora, as detailed below. in our subsequent rewriting experiments, we then use a corpus of movie summaries for the reconstruction objective as well as a parallel paraphrase corpus for the paraphrasing objective. the paraphrase corpus is only used during training, whereas the movie summaries also serve to validate and test rewriting models. ## evaluation this section reports experiments on agency and power classification and on the generation counter-factuals for gender bias mitigation. the main goal is to evaluate our joint agency-power approach to sentence rewriting in light of the state of the art. ## conclusion in this work, we have studied how to rewrite sentences to adjust the agency and power of their subjects jointly. to this end, we have developed a new candidate verb identification method that fosters a meaning-preserving adaptation of both connotations in transformer-based generation. by employ-ing classifiers for agency and power, our rewriting approach can handle any given verb in the current sentence context, unlike previous approaches. our experiments have stressed the importance of addressing agency and power jointly. in automatic evaluation, the proposed approach has turned out competitive in agency to the previous state of the art, while effectively controlling for power for the first time. in manual evaluation, human annotators favored the sentences rewritten by our approach in terms of all relevant dimensions: target agency, target power, and meaning preservation. we thus conclude that our approach contributes towards the generation of counterfactuals that can be used for gender bias mitigation, as shown in previous work. yet, the results leave room for improvements regarding both connotations, which should be addressed in future work. for a refined evaluation, more extensive agency and power lexicons may be needed. to facilitate the lexicon creation process, the classifiers may be used to suggest labels prior to human post-checking. appr. w/o cls. johnny, enraged with the way joe and jake treat ginny, attacks them. approach johnny, enraged with the way joe and jake treat ginny, defend them. finally, the data used in our experiments restricts the generalizability of our results to some extent. in particular, we analyzed the effectiveness of our method on english movie summaries only. other genres as well as other languages may lead to different behavior, although we do not see an immediate reason why it should not work there. as far as the availablity of data will permit, we plan to do further experiments in other settings in the future.