构建职业性别偏见知识图谱，揭示机器翻译中的性别刻板印象
GOSt-MT: A Knowledge Graph for Occupation-related Gender Biases in Machine Translation
- 整合真实劳动力数据与训练语料，构建跨语言性别统计图谱
- 发现英法希三语中职业与性别的不均衡映射现象
- 为消除翻译系统性别偏见提供可操作分析框架，适合伦理研究者使用
机器翻译中的性别偏见带来显著挑战，常强化有害刻板印象。在劳动领域，职业常被错误关联特定性别，加剧传统性别观念，对社会产生深远影响。本文提出一种新方法，通过构建GOSt-MT（用于机器翻译的性别与职业统计数据）知识图谱，融合真实劳动力数据与机器翻译训练语料中的性别统计数据。该图谱支持对英语、法语和希腊语中职业相关性别偏见的详细分析，有助于识别持续存在的刻板印象及需干预的环节。通过提供职业在劳动力市场与机器翻译系统中性别化的结构化分析框架，GOSt-MT助力实现更公平、减少性别偏见的机器翻译系统。
原文摘要 · Abstract (English)
Gender bias in machine translation (MT) systems poses significant challenges that often result in the reinforcement of harmful stereotypes. Especially in the labour domain where frequently occupations are inaccurately associated with specific genders, such biases perpetuate traditional gender stereotypes with a significant impact on society. Addressing these issues is crucial for ensuring equitable and accurate MT systems. This paper introduces a novel approach to studying occupation-related gender bias through the creation of the GOSt-MT (Gender and Occupation Statistics for Machine Translation) Knowledge Graph. GOSt-MT integrates comprehensive gender statistics from real-world labour data and textual corpora used in MT training. This Knowledge Graph allows for a detailed analysis of gender bias across English, French, and Greek, facilitating the identification of persistent stereotypes and areas requiring intervention. By providing a structured framework for understanding how occupations are gendered in both labour markets and MT systems, GOSt-MT contributes to efforts aimed at making MT systems more equitable and reducing gender biases in automated translations.
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