NLP多样性研究过度聚焦公平，忽视其他领域不平等
NLP needs Diversity outside of 'Diversity'
- 分析NLP各子领域研究人员构成，揭示非公平领域边缘化现象
- 发现地理与语言壁垒导致多元参与不足，形成恶性循环
- 建议打破反馈循环，推动全领域包容性发展
本文指出，近年来自然语言处理（NLP）领域的多样性进展过度集中于公平相关议题。我们进一步论证，这是由一系列激励机制、偏见和障碍共同导致的结果，使非公平领域的边缘化研究者难以参与，或被迫转向公平相关方向。通过分析不同子领域研究人员的构成数据，本文支持多项建议：打破强化不平等的反馈循环，解决地理与语言障碍，以促进NLP所有领域实现真正的包容与公平。
原文摘要 · Abstract (English)
This position paper argues that recent progress with diversity in NLP is disproportionately concentrated on a small number of areas surrounding fairness. We further argue that this is the result of a number of incentives, biases, and barriers which come together to disenfranchise marginalized researchers in non-fairness fields, or to move them into fairness-related fields. We substantiate our claims with an investigation into the demographics of NLP researchers by subfield, using our research to support a number of recommendations for ensuring that all areas within NLP can become more inclusive and equitable. In particular, we highlight the importance of breaking down feedback loops that reinforce disparities, and the need to address geographical and linguistic barriers that hinder participation in NLP research.
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