The Gender- GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 Languages (2023)

Abstract: Gender biases in language generation systems are challenging to mitigate. One possible source for these biases is gender representation disparities in the training and evaluation data. Despite recent progress in documenting this problem and many attempts at mitigating it, we still lack shared methodology and tooling to report gender representation in large datasets. Such quantitative reporting will enable further mitigation, e.g., via data augmentation. This paper describes the Gender-Gap Pipeline (for Gender-Aware Polyglot Pipeline), an automatic pipeline to characterize gender representation in large-scale datasets for 55 languages. The pipeline uses a multilingual lexicon of gendered person-nouns to quantify the gender representation in text. We showcase it to report gender representation in WMT training data and development data for the News task, confirming that current data is skewed towards masculine representation. Having unbalanced datasets may indirectly optimize our systems towards outperforming one gender over the others. We suggest introducing our gender quantification pipeline in current datasets and, ideally, modifying them toward a balanced representation.

## introduction despite their widespread adoption, natural language processing (nlp) systems are typically trained on data with social and demographic biases. such biases inevitably propagate to our models and their generated outputs, e.g., by over-representing a given demographic group and under-representing others. it is, therefore, critical to measure, report, and design methods to mitigate these biases, before they can be encoded and potentially amplified 1 http://www2.statmt.org/wmt23/ 2 the gender-gap pipeline is available at https: //github.com/facebookresearch/responsiblenlp/ tree/main/gender_gap_pipeline dataset 0.16% 0.34% 0.16% 0.3% 0.34% ## related work the study of biases in text has become more important in recent years, with large language models (llms) displaying bias against people depending on their demographics and identity. as a testament to the importance of this topic, many recent papers, including those introducing gpt-3 and 4 @xcite , palm 1 and 2 @xcite , llama 1 and 2 @xcite , analyze how such biases affect their model outputs. some works even discuss frequencies of gendered terms in their pretraining corpora @xcite , as this can affect downstream generation. despite this acknowledgment of the issue, general purpose tools to measure demographic biases are still fairly rare, and so far have mainly been in english. however, some have begun to measure demographic biases beyond english. @xcite built a comprehensive analysis dataset covering 13 demographic groups and @xcite extended it to the multilingual setting. specific to machine translation, @xcite discussed best practices in reporting gender bias. several works @xcite @xcite have explored metrics for exposing failures in automatically translating pronoun and occupations, and some have even explored mt model training @xcite or fine-tuning @xcite ; costa-jussà and de jorge, 2020) or both @xcite to lessen the effect of gender-related biases. more than this, there are initiatives that provide toolkits to generate multilingual balanced datasets in terms of gender @xcite from wikipedia and even balanced in gender within occupations @xcite . however, despite the progress made, most of these resources only cover a handful of languagesthe community still lacks easy to use, open-source toolkits to measure biases across a large number of languages. in this work, we address this need by showcasing, gender-gap, a lexical matching pipeline to measure gender distribution across 55 languages. 3 proposed data collection and pipeline ## experiments to showcase gender-gap, we run it on common crawl raw data and two popular machine translation evaluation datasets: flores-200 @xcite and ntrex-128 @xcite . flores is a wikipedia-based dataset including 3001 sentences translated from english to 200 languages. ntrex-128 is made of 1997 sentences from news documents originally collected for wmt 2019 @xcite translated from english into 128 languages. both these datasets are part of the corpora provided by the wmt shared task. in addition, we run the pipeline on a sample of common crawl. 10 common crawl is a snapshot of crawlable web data that is widely used in the nlp community thanks to the release of the ccnet corpora @xcite , the oscar corpus @xcite and the c4 corpus @xcite . it is used to train nlp systems like language and machine translation models. we run our pipeline on 100k documents for each language. our pipeline supports 55 languages, and we run it on the intersection of these datasets with the set of supported languages. ## conclusion in this work, we presented gender-gap, a large scale multilingual pipeline to compute gender distribution across 55 languages. we find that broadly used datasets are biased toward masculine gender. based on this finding, our primary recommendation for multilingual nlp practitioner is to report the gender distribution along with the performance score. this allows reader and systems adopters to be aware of these biases in order to integrate this in their system deployment. secondly, based on our multilingual lexicon, many directions could be taken to mitigate biases in the performance of the systems (due to biases in the data). @xcite developed a perturbation-based technique to build nlp systems that are less biased toward specific group. we envision using our multilingual lexicon to adapt this technique beyond english.