Gender Bias in Machine Translation (2104.06001v3)

Abstract: Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, elaborating and communicating information. However, it can suffer from biases that harm users and society at large. As a relatively new field of inquiry, gender bias in MT still lacks internal cohesion, which advocates for a unified framework to ease future research. To this end, we: i) critically review current conceptualizations of bias in light of theoretical insights from related disciplines, ii) summarize previous analyses aimed at assessing gender bias in MT, iii) discuss the mitigating strategies proposed so far, and iv) point toward potential directions for future work.

Gender Bias in Machine Translation

In exploring the persistent issue of gender bias within machine translation (MT), the paper by Savoldi et al. provides a comprehensive review of existing research while proposing a framework to guide future studies. The authors advocate for a harmonized approach, integrating interdisciplinary insights to address the multifaceted impacts of gender bias and potential strategies for mitigation.

The paper delineates a methodological approach encompassing four key objectives:

1. Critical Review of Bias Conceptualizations: The paper evaluates prevailing definitions of bias in MT and adjacent fields. It posits bias as a divergence from an expected outcome, underscoring the ethical and sociolinguistic dimensions of what constitutes harmful bias in technology.
2. Evaluation of Previous Studies on Gender Bias in MT: Past works demonstrate systematic gender bias, with MT models defaulting to masculine pronouns or occupational stereotypes, such as 'engineers' translated as male and 'nurses' as female. These biases surface when translating from gender-neutral languages to those with grammatical gender, illustrating societal stereotypes embedded within MT systems.
3. Discussion on Mitigation Strategies: Various technical interventions are proposed and evaluated. Approaches such as gender tagging, context augmentation, and balanced fine-tuning show promise. However, these are hindered by practical challenges like the need for extensive metadata and compatibility with real-world deployment.
4. Directions for Future Research: Lastly, the paper suggests avenues for further exploration, emphasizing non-binary gender inclusivity and the role of interdisciplinary methods in jointly tackling the ethical dimensions of gender bias. Machine learning biases persist across diverse AI applications, calling for technology development informed by social equity considerations.

The implications of this research are both practical and theoretical. Practically, improved gender-aware MT could alleviate representational harms and ensure equitable service quality across genders. Theoretically, the proposed framework could fuel robust inquiry into the socio-cultural dimensions of language, gender, and technology. Continued interdisciplinary collaboration will be instrumental in advancing these understanding, potentially influencing broader AI contexts and applications.

The paper underscores the necessity of detailed, socially-informed approaches to both assessing and mitigating gender bias. It gives clear evidence that recognizing gender-related subtleties in data and models requires rigorous, context-sensitive methodologies. Furthermore, the paper posits that combating bias effectively demands ongoing engagement with diverse linguistic communities, ensuring translational outputs resonate authentically with all users.