P ower T ransformer: Unsupervised Controllable Revision for Biased Language Correction (2020)

Abstract: Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction. For example, a female character in a story is often portrayed as passive and powerless (“_She daydreams about being a doctor_”) while a man is portrayed as more proactive and powerful (“_He pursues his dream of being a doctor_”). We formulate **Controllable Debiasing**, a new revision task that aims to rewrite a given text to correct the implicit and potentially undesirable bias in character portrayals. We then introduce PowerTransformer as an approach that debiases text through the lens of connotation frames (Sap et al., 2017), which encode pragmatic knowledge of implied power dynamics with respect to verb predicates. One key challenge of our task is the lack of parallel corpora. To address this challenge, we adopt an unsupervised approach using auxiliary supervision with related tasks such as paraphrasing and self-supervision based on a reconstruction loss, building on pretrained language models. Through comprehensive experiments based on automatic and human evaluations, we demonstrate that our approach outperforms ablations and existing methods from related tasks. Furthermore, we demonstrate the use of PowerTransformer as a step toward mitigating the well-documented gender bias in character portrayal in movie scripts.

## introduction narratives and news texts often reflect societal biases and stereotypes, such as the traditional gender role that women are passive and submissive @xcite @xcite . the task of controllable text revision, i.e., rephrasing text to a targeted style or framing, can help correct for these biases by altering and equalizing the way both authors contributed equally. ## controllable debiasing controllable debiasing is a novel formalization of stylistic rewriting that aims to debias the portrayal of characters through controllable revision. to achieve the desired character portrayal, a system must be able to change the underlying meaning of events, unlike certain formalizations (e.g., politeness transfer; @xcite where full meaning preservation is required. without this, systems run the risk of merely paraphrasing the biases in text. however, revisions must be precise and avoid unnecessary meaning changes, which can often occur in stylistic rewriting (e.g., reversing the sentiment of a review drastically changes its underlying meaning). for our new rewriting task of changing portrayal bias, we focus on connotation frames that measure the power and agency ascribed to characters through the actions they take. connotation frames @xcite distill implicit relations between a verb, its agent, and its theme. in this work, we use the positive, neutral, and negative agency dimensions, where agency is defined as the capacity to intentionally make changes or act upon one's environment @xcite . for example, illustrated in figure 1 , "x pursued y" implies that x has positive agency. 1 using machine-in-the-loop writing systems (e.g., ghazvininejad et al., 2016, 2017; clark et al., 2018, textio 2 ), models trained on this task could help authors write news, stories, or movies that portray characters in less biased ways, and thereby help mitigate the negative effects of stereotypical portrayals in media @xcite . ## powertransformer we present a new approach for controllable debiasing called powertransformer, which addresses two key challenges: the paucity of parallel supervised data for training and the difficulty of incorporating fine-grained control for steering the agency of the output. our approach (figure 2 ) jointly learns to reconstruct partially masked story 1 future work could explore using the power dimension instead of agency, or alternative operationalizations of biases, e.g., social bias frames @xcite or regard towards minorities as introduced by sheng et al. (2019). 2 https://textio.com/ joint reconstruction + paraphrase objective at training time transformer inputs issa enjoyed football growing up. -gpt transformer vocab boosting at decoding time next word logits boosted logits agency scaling + 𝛃aw issa played football growing up. + masking target agency positive @xmath0. <@xmath1: overview of the full powertransformer model. an input sentence is masked for verb tokens indicative of agency. masked inputs and target agency are used as gpt inputs. we use a joint objective using both paraphrase data and masked input sentences for training. at decoding time, we employ a vocab boosting technique to steer generations towards the target agency. ## controllable debiasing experiments in this section, we describe three experiments for investigating powertransformer performance. first, we evaluate performance of our full model and ablated baselines, using automatic metrics to quantify the effectiveness of each modelling component ( §4.4). next, we compare our full model to baselines from related work ( §4.5). lastly, given the limitations of automated metrics for evaluating generations @xcite , we obtain human judgments of model performance through crowdsourcing ( §4.6). we additionally include examples of generations in ## gender bias in movies as a proof-of-concept of controllable debiasing, we investigate whether gender biases in portrayals of movie characters can be mitigated using pow-ertransformer. ## related work controllable debiasing is a new formalization of the unsupervised stylistic rewriting task, contrasting with supervised approaches which benefit from parallel corpora (e.g., @xcite @xcite . in unsupervised settings, a majority of work has dealt with the dearth of parallel data by using encoderdecoder setups paired with discriminators to disentangle style from content and steer generations (e.g., @xcite @xcite @xcite @xcite or backtranslation setups @xcite . in contrast, @xcite introduce a modular approach (later adapted to transformer models by @xcite that relies on drop-in replacement of attribute markers followed by language correction. power-transformer improves on this approach with an additional out-of-domain paraphrasing objective. while a majority of related existing stylistic rewriting work defines style as sentiment (e.g., on reviews), a notable exception is nogueira dos @xcite , who use stylistic rewriting to make text less hateful or offensive. similar in spirit, controllable debiasing is a novel formalization that aims to address and revise social biases expressed in text, but using the nuanced implications distilled in connotation frames of power and agency instead of binary offensiveness. our work also draws inspiration from controllable generation methods (e.g., @xcite @xcite . while those methods steer the generation output to contain desired attributes, controllable revision is constrained to revise an input sentence in addition to generating with desired attributes. ## conclusion we introduce a new text revision task of controllable debiasing, to help debias the portrayal of characters through the lens of connotation frames of power and agency. to this end, we create pow-ertransformer, a transformer-based encoderdecoder trained on a joint reconstruction and paraphrasing objective. our approach demonstrates promising results to revise sentences with targeted power and agency, and outperforms ablations and baselines on both automatic and human evaluations. finally, as a case study, we show the feasibility for controllable debiasing at debiasing the portrayal of characters in movie scripts. our findings highlight the potential of neural models as a tool for editing out social biases in text.