  Disparities in authorship and citations across gender can have substantial
adverse consequences not just on the disadvantaged genders, but also on the
field of study as a whole. Measuring gender gaps is a crucial step towards
addressing them. In this work, we examine female first author percentages and
the citations to their papers in Natural Language Processing (1965 to 2019). We
determine aggregate-level statistics using an existing manually curated
author--gender list as well as first names strongly associated with a gender.
We find that only about 29% of first authors are female and only about 25% of
last authors are female. Notably, this percentage has not improved since the
mid 2000s. We also show that, on average, female first authors are cited less
than male first authors, even when controlling for experience and area of
research. Finally, we discuss the ethical considerations involved in automatic
demographic analysis.
