Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model (2023)

Abstract: Natural language generation models reproduce and often amplify the biases present in their training data. Previous research explored using sequence-to-sequence rewriting models to transform biased model outputs (or original texts) into more gender-fair language by creating pseudo training data through linguistic rules. However, this approach is not practical for languages with more complex morphology than English. We hypothesise that creating training data in the reverse direction, i.e. starting from gender-fair text, is easier for morphologically complex languages and show that it matches the performance of state-of-the-art rewriting models for English. To eliminate the rule-based nature of data creation, we instead propose using machine translation models to create gender-biased text from real gender-fair text via round-trip translation. Our approach allows us to train a rewriting model for German without the need for elaborate handcrafted rules. The outputs of this model increased gender-fairness as shown in a human evaluation study.

## introduction from facial recognition to job matching and medical diagnosis systems, numerous real-world applications suffer from machine learning models that are discriminative towards minority groups based on characteristics such as race, gender, or sexual orientation @xcite . in natural language processing (nlp), gender bias is a particularly significant issue @xcite . while research in psychology, linguistics and education studies demonstrates that inclusive language can increase the visibility of women @xcite and encourage young individuals of all genders to pursue stereotypically gendered occupations @xcite without s any text t artificial de-biased text augment (complex rules, parsing, ...) any text t real unbiased text filter (simple rules) s artificial biased text augment (complex rules, parsing, ...) (b) backward augmentation (this work) any text t real unbiased text filter (simple rules) s artificial biased text translate biased mt p artificial biased text (pivot language) translate biased mt (c) round-trip augmentation (this work) figure 1 : de-biasing rewriters can be implemented as neural sequence-to-sequence models trained on source (s) to target (t) text examples. previous work creates artificial t from real s through complex augmentation @xcite . we propose to use real t and generate artificial s to accommodate morphologically complex languages and avoid target-side noise (b). furthermore, we show that by leveraging biased off-the-shelf machine translation (mt) models, complex rules can be avoided altogether to generate training data for de-biasing rewriters (c). there is a variety of work on correcting gender bias in generative models. some approaches include curating balanced training data @xcite , de-biasing models with modifications to the training algorithms @xcite , and developing better inference procedures @xcite . the focus of our work lies on so-called rewriting models, yet another line of debiasing research that revolves around models that map any input text (e.g., the output of a biased generative model) to a gender-fair version of the same text. the main challenge here is training data: due to the lack of large amounts of parallel biasedunbiased text segments, previous work (section 2) produces the latter through handcrafted rules (forward augmentation, figure 1a ). we identify two key problems with the forward augmentation paradigm. first, the rule-based de-biasing of real-world text comes at a risk of introducing target-side noise, which tends to degrade output quality more than source-side noise @xcite . second, while already intricate for english, it is likely even harder to define de-biasing rules for more morphologically complex languages with grammatical gender (figure 2 ). an approach proposed by @xcite , for example, requires morphological, dependency and co-reference analysis, as well as named entity recognition and a word inflexion database. in this paper, we propose two modifications to the prevalent data augmentation paradigm and we find that biased models can be used to train debiasing rewriters in a simple yet effective way. our three main contributions are: the policemen search for a capable leader. the police officers search for a capable leader. die polizisten suchen einen fähigen leiter. die polizist*innen suchen eine*n fähige*n leiter*in. ## background gender-fair foot_0 rewriting is a conditional text generation problem. it can be approached with conventional sequence-to-sequence models trained on large amounts of parallel data, i.e., biased segments s and their gender-fair counterparts t. since such corpora do not exist in practice, 3 @xcite and @xcite create artificial gender-fair target segments t pseudo from existing source segments s with a rule-based de-biasing pipeline (forward augmentation, figure 1a ). the sequence-to-sequence model trained on the outputs of this pipeline removes the need for computationally expensive toolchains (such as dependency parsing) at runtime and increases robustness towards noisy inputs @xcite . ## backward augmentation we hypothesise that the data augmentation direction for gender-fair rewriters can be reversed (figure 1b ) without a negative impact on quality. our motivation is rooted in work on data augmentation for mt @xcite , where backtranslation of monolingual target text tends to result in better quality than forward-translation of original source text @xcite . we use backward augmentation to train a gender-fair rewriter model for english and compare its performance to the forward augmentation approach proposed by @xcite and @xcite . ## round-trip augmentation artificially biasing gender-fair target segments rather than de-biasing gender-biased source segments is especially useful for languages with grammatical gender and more complex morphology than english. taking german as a running example, we would need some form of animacy prediction in forward augmentation to transform ambiguous nouns such as "leiter" only if they refer to a person ("leader") and not to an object ("ladder"). in backward augmentation, we do not need an animacy prediction model since this information is implicitly encoded in the gender-fair forms, as seen in figure 2 . nevertheless, defining rules for mapping gender-fair segments to gender-biased segments or vice versa requires expert knowledge and will likely never completely cover morphologically complex languages. as an alternative to handcrafting rules, we propose to exploit the fact that current mt models generate inherently biased text: we create pseudo source segments via round-trip translation through a pivot language that (mostly) does not mark gender (figure 1c ). we use this method to train a gender-fair rewriter model for german, and benchmark it against a highly engineered fully rule-based baseline (diesner-mayer and seidel, 2022). ## discussion our experimental findings yield insights for future research and design implications for real-world nlp applications. biased models are useful for de-biasing. at least in the subfield of gender-fair rewriting, debiasing research has focussed extensively on human annotation @xcite and rule-based processing and training data creation (e.g., @xcite @xcite @xcite . conversely, our work demonstrates that robust de-biasing rewriters can be implemented by leveraging inherently biased nlp models. our round-trip augmentation experiment covers a single language, but we note that both the training data @xcite and mt models @xcite we leverage are readily available for many other languages. we also assume that (biased) mt models -the only requirement for gender-fair rewriters based on round-trip augmentation apart from simple data filters -are available for more languages or are easier to create than the different nlp tools used in typical rule-based augmentation pipelines, such as robust models for lemmatisation, morphological analysis, dependency parsing, named entity recognition, and co-reference resolution. rule-based de-biasing lacks robustness. handwritten rules are limited by design. for example, a breakdown of the results shown in users prefer errors over bias. potential beneficiaries of gender-fair rewriters -the 294 participants of our human evaluation campaign -rated the outputs of our german rewriter as more genderfair than the biased original texts and explicitly (in the post-experiment survey) stated they prefer (potentially erroneous) gender-fair forms over errorfree non-gender-fair forms. this is an important finding because our model is far from perfect, as evidenced by a high error rate compared to english and manual inspection of the outputs used in the evaluation campaign (appendix h). previous work has found that non-binary people consider nlp as harmful, particularly due to the risk of being misgendered by mt outputs @xcite . @xcite caution that genderfair rewriters may not be applicable to languages other than english because "few languages have a crystallized approach when it comes to genderneutral pronouns and gender-neutral word endings." while there is an active debate (and no established standard) about the form that gender-fair german should take (e.g., @xcite , our evaluation campaign makes a strong case for using nlp technology -even if not flawless -to offer one form of de-biased text in real-world applications. since rewriters are relatively lightweight models that operate independently from any input provider, be it a human author or a biased mt model, they would seem suitable for integration into a wide range of systems with reasonable effort. ## conclusions and future work despite an impressive performance in a wide range of tasks and applications, state-of-the-art nlp models contain numerous biases @xcite @xcite . our work shows that knowledge of a bias can be used to correct that bias with the biased models themselves. in the case of gender-fair rewriting, we demonstrated that reversing the data augmen-tation direction and using round-trip translations from biased mt models can substitute the prevalent rewriting paradigm that relies on handcrafted and often complex rules on top of morphological analysers, dependency parsers, and many other nlp tools. in our case study for german, our model surpasses the performance of a strong baseline in terms of wer and produces outputs that were perceived as more gender-fair than unaltered biased text in a human evaluation campaign with 294 potential beneficiaries. while our approach enables the application of gender-fair rewriting to any language for which translation models exist, we believe there are several other uses cases where biased models can be leveraged for de-biasing, including dialect rewriting @xcite , subjective bias neutralisation @xcite , and avoiding discrimination in dialogue systems @xcite .