超越善意：多语言与低资源自然语言处理研究的框架何时沦为讽刺画？
Beyond Good Intentions: When Does the Framing of Multilingual and Low-Resource NLP Research Become a Caricature?
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中文总结 AI 辅助
本文考察近期NLP与ML论文，提出研究框架分析框架，发现低资源NLP研究常优先资源创建而非社区受益证据，最后给出评估断言的实用建议。
中文摘要 AI 辅助
为低资源语言构建语言技术并开展自然语言处理（NLP）研究——尤其当研究由母语使用者主导或涉及参与式研究实践时——常被描述为解决不平等、服务当地社区，有时还被视为对去殖民化的贡献。本文中，我们考察了近期发表的NLP与机器学习（ML）论文，重点关注用于描述多语言性、低资源语言及代表性不足文化的叙事。我们提出了一个用于分析研究框架的框架，并识别出可能阻碍问责制、限制服务不足社区（为社区及由社区开展的）公平知识生产的反复出现的修辞模式。我们进一步评估了关于社区受益的断言的证据基础，发现此类陈述往往支持薄弱或未被证实。尽管社区所有权与参与常被作为关键目标，我们基于ACL Anthology统计数据的分析表明，研究成果更常优先考虑资源创建与基准测试——这些是重要但不同的目标——而非更广泛结构变革的证据。我们最后提出了实用建议，以帮助作者、审稿人和读者批判性评估这些断言，避免潜在误导性的框架。
英文摘要
Building language technologies and conducting NLP research for low-resource languages---particularly when led by native speakers or involving participatory research practices---are often framed as means of addressing inequality, serving local communities, and, at times, contributing to *decolonisation*. In this paper, we examine recently published NLP and ML papers, focusing on the narratives used to characterise multilinguality, low-resource languages, and underrepresented cultures. We propose a framework for analysing research framings and identify recurring rhetorical patterns that may hinder accountability and constrain equitable knowledge production for---and by---underserved communities. We further assess the evidential basis of assertions regarding community benefit and find that such statements are often weakly supported or left unsubstantiated. Although community ownership and participation are frequently presented as key objectives, our analysis, supported by statistics from the ACL Anthology, suggests that research outputs more often prioritise resource creation and benchmarking---important but distinct goals---over evidence of broader structural change. We conclude by offering practical recommendations to help authors, reviewers, and readers critically assess these assertions and avoid potentially misleading framings.
发表机构
- Cardiff University（卡迪夫大学）
- University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。
