  A key aim of science is explanation, yet the idea of explaining language
phenomena has taken a backseat in mainstream Natural Language Processing (NLP)
and many other areas of Artificial Intelligence. I argue that explanation of
linguistic behaviour should be a main goal of NLP, and that this is not the
same as making NLP models explainable. To illustrate these ideas, some recent
models of human language production are compared with each other. I conclude by
asking what it would mean for NLP research and institutional policies if our
community took explanatory value seriously, while heeding some possible
pitfalls.
