梳理欧非语言机器翻译中的偏见检测与缓解现状，指出研究集中于少数语言。
Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Review
- 聚焦欧非语言的偏见检测与缓解方法
- 多数研究仅覆盖少数主流语言
- 呼吁拓展对小语种的研究以提升多样性
研究自然语言处理中偏见检测与缓解，特别是机器翻译领域的相关方法极具现实意义，因社会刻板印象可能被这些系统反映或强化。本文分析了该领域的最新进展，重点关注欧洲和非洲语言。研究发现，当前大多数工作集中于少数语言，而未充分涵盖许多较少研究的语言。这表明未来研究有潜力通过覆盖更多非主流语言，推动该领域研究的多元化发展。
原文摘要 · Abstract (English)
Studying bias detection and mitigation methods in natural language processing and the particular case of machine translation is highly relevant, as societal stereotypes might be reflected or reinforced by these systems. In this paper, we analyze the state-of-the-art with a particular focus on European and African languages. We show how the majority of the work in this field concentrates on few languages, and that there is potential for future research to cover also the less investigated languages to contribute to more diversity in the research field.
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