  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.
