分析顶会NLP论文的学术与社会影响，发现语言建模最广泛，伦理议题在政策中受关注但学界引用少。
Internal and External Impacts of Natural Language Processing Papers
- 通过引用分析学术、专利、媒体和政策文档，评估NLP研究的内外部影响。
- 语言建模在学术和外部领域影响力最大，语言基础类研究影响较弱。
- 伦理、偏见等议题在政策文件中受重视，但学术引用较少，外部偏好各异。
我们研究了1979至2024年间发表于顶级会议（ACL、EMNLP、NAACL）的NLP研究成果的影响。通过分析来自科研论文以及专利、媒体和政策文件的引用，考察不同NLP主题在学术界与更广泛公众中的传播程度。结果表明，语言建模在内部和外部影响范围上均最广，而语言学基础类主题影响较弱。尽管多数主题的内部与外部影响趋势一致，但伦理、偏见与公平性等议题在政策文件中受到显著关注，而在学术文献中的引用却很少。此外，外部领域表现出不同偏好：专利聚焦实际应用，媒体与政策文件更关注NLP模型的社会影响。
原文摘要 · Abstract (English)
We investigate the impacts of NLP research published in top-tier conferences (i.e., ACL, EMNLP, and NAACL) from 1979 to 2024. By analyzing citations from research articles and external sources such as patents, media, and policy documents, we examine how different NLP topics are consumed both within the academic community and by the broader public. Our findings reveal that language modeling has the widest internal and external influence, while linguistic foundations have lower impacts. We also observe that internal and external impacts generally align, but topics like ethics, bias, and fairness show significant attention in policy documents with much fewer academic citations. Additionally, external domains exhibit distinct preferences, with patents focusing on practical NLP applications and media and policy documents engaging more with the societal implications of NLP models.
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