  Despite inextricable ties between race and language, little work has
considered race in NLP research and development. In this work, we survey 79
papers from the ACL anthology that mention race. These papers reveal various
types of race-related bias in all stages of NLP model development, highlighting
the need for proactive consideration of how NLP systems can uphold racial
hierarchies. However, persistent gaps in research on race and NLP remain: race
has been siloed as a niche topic and remains ignored in many NLP tasks; most
work operationalizes race as a fixed single-dimensional variable with a
ground-truth label, which risks reinforcing differences produced by historical
racism; and the voices of historically marginalized people are nearly absent in
NLP literature. By identifying where and how NLP literature has and has not
considered race, especially in comparison to related fields, our work calls for
inclusion and racial justice in NLP research practices.
