The Gender-GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 Languages (2308.16871v1)

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.

• The paper introduces the Gender-Aware Polyglot Pipeline to quantify gender bias across 55 languages using a multilingual lexicon and lexical matching.
• It applies the pipeline to datasets like FLORES-200 and Common Crawl, revealing a pervasive masculine bias in several languages.
• The research advocates for reporting gender distribution in NLP systems to improve fairness and transparency in model evaluation.

Insights from "The Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 Languages"

This paper offers a detailed examination of the challenges and methodologies related to assessing gender representation in multilingual datasets, specifically addressing the biases inherent in NLP systems. The authors introduce the "Gender-Aware Polyglot Pipeline" (\Pipeline), a novel tool designed to evaluate gender representation across a wide array of languages and global datasets. This work builds on the recognition that gender biases in LLMs often stem from training datasets that do not adequately reflect diverse gender representation.

Methodology and Pipeline Overview

The \Pipeline comprises two main components: a multilingual gender lexicon and a lexical matching pipeline. The authors constructed a multilingual lexicon by translating a baseline list of English gendered nouns, drawn from the HolisticBias dataset, into 55 languages. This lexicon is categorized into three distinct gender classes: masculine, feminine, and unspecified. Each noun’s gender classification is adapted for the linguistic and cultural context of the target language.

For gender quantification, the pipeline uses lexical matching to identify and tally occurrences of gendered terms within texts, delivering representative statistics of gender distribution. By leveraging tools like Stanza for word segmentation, the process identifies gendered terms in input datasets at a granular level, producing aggregate gender representation statistics.

Empirical Results

The authors applied the \Pipeline to three major datasets: FLORES-200, NTREX-128, and a subset of Common Crawl. Across these datasets, the findings reveal a consistent skew towards masculine gender representation. For instance, in the Common Crawl sample, masculine terms frequently outnumber feminine ones. Notably, around 16 out of the 54 languages analyzed exhibited a consistent masculine bias in all datasets.

Moreover, significant disparities in gender representation were observed across different languages and domains. Languages such as Spanish, French, and many others exhibited more skew towards masculine representations. In contrast, certain datasets demonstrated more variability, suggesting the impact of domain-specific data distributions on gender representation.

Implications and Future Research

The findings of this paper underscore the necessity for NLP practitioners to report gender distribution along with performance metrics. Such transparency will allow stakeholders to understand and potentially mitigate biases in the deployment of NLP systems, which is vital for fostering equitable technology.

From a practical standpoint, the \Pipeline offers a tool that researchers can integrate into existing workflows to detect and address gender biases in multilingual datasets, thereby facilitating more equitable model training and evaluation. The research prompts further exploration into alternative methodologies to refine gender representation analysis and mitigation across languages, potentially expanding the lexicon to include gender-related terms beyond person nouns.

Speculation on Future Developments

The continuous evolution of NLP and AI demands persistent emphasis on inclusive data practices. Enhancements to the \Pipeline could include expansion to more nuanced gender classes and increased contextual sensitivity to linguistic diversity. Additionally, further studies could explore dynamically adapting gender classification in real-time translational contexts.

Given the practical utility of the \Pipeline, integrating it with broader AI fairness frameworks could enable comprehensive bias reduction strategies in various applications. Collaborative, cross-disciplinary efforts will be critical to achieve advances in the fair representation of diverse gender identities across languages and cultures in NLP systems.