剖析多语言与低资源NLP研究中的叙事陷阱，揭示理想化表述背后的证据缺失。
Beyond Good Intentions: When Does the Framing of Multilingual and Low-Resource NLP Research Become a Caricature?
- 构建分析框架，识别研究中常见的夸大性叙事模式。
- 统计ACL论文发现：多数研究侧重资源建设而非真实社区变革。
- 呼吁作者和评审者警惕空泛承诺，强调实证支持的重要性。
针对低资源语言的自然语言处理研究常被描述为减少不平等、服务本地社群乃至推动去殖民化的途径。本文分析近期发表的NLP与机器学习论文，聚焦其对多语言、低资源语言及边缘文化所采用的叙述方式。提出一个研究框架以识别可能削弱问责制并限制公平知识生产的重复修辞模式。进一步评估关于社区获益的主张，发现这些说法往往缺乏充分证据或未加验证。尽管社区主导与参与常被列为关键目标，但基于ACL文集的统计数据表明，研究产出更倾向于资源创建与基准测试——虽重要，却不同于结构性变革。文章最后提供实用建议，帮助作者、审稿人和读者批判性评估此类宣称，避免误导性框架。
原文摘要 · Abstract (English)
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.
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