Natural Language Processing (NLP) is poised to substantially influence the
world. However, significant progress comes hand-in-hand with substantial risks.
Addressing them requires broad engagement with various fields of study. Yet,
little empirical work examines the state of such engagement (past or current).
In this paper, we quantify the degree of influence between 23 fields of study
and NLP (on each other). We analyzed ~77k NLP papers, ~3.1m citations from NLP
papers to other papers, and ~1.8m citations from other papers to NLP papers. We
show that, unlike most fields, the cross-field engagement of NLP, measured by
our proposed Citation Field Diversity Index (CFDI), has declined from 0.58 in
1980 to 0.31 in 2022 (an all-time low). In addition, we find that NLP has grown
more insular -- citing increasingly more NLP papers and having fewer papers
that act as bridges between fields. NLP citations are dominated by computer
science; Less than 8% of NLP citations are to linguistics, and less than 3% are
to math and psychology. These findings underscore NLP's urgent need to reflect
on its engagement with various fields.

量化分析了 NLP 和其他 23 个学科领域（相互）之间的影响程度，结果显示 NLP 的学科交叉合作程度下降，自 1980 年的 0.58 降至 2022 年的 0.31（历史最低），同时 NLP 的学术引用越来越内向，主要引用计算机科学领域的论文，而对语言学、数学和心理学等领域的引用较少，这些研究结果凸显了 NLP 需要紧急反思与其他学科领域的合作交流。