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Natural Language Processing (NLP) is poised to substantially influence theworld. 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 studyand NLP (on each other). We analyzed ~77k NLP papers, ~3.1m citations from NLPpapers to other papers, and ~1.8m citations from other papers to NLP papers. Weshow that, unlike most fields, the cross-field engagement of NLP, measured byour proposed Citation Field Diversity Index (CFDI), has declined from 0.58 in1980 to 0.31 in 2022 (an all-time low). In addition, we find that NLP has grownmore insular -- citing increasingly more NLP papers and having fewer papersthat act as bridges between fields. NLP citations are dominated by computerscience; Less than 8% of NLP citations are to linguistics, and less than 3% areto math and psychology. These findings underscore NLP's urgent need to reflecton its engagement with various fields.

We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields