  In a fair world, people have equitable opportunities to education, to conduct
scientific research, to publish, and to get credit for their work, regardless
of where they live. However, it is common knowledge among researchers that a
vast number of papers accepted at top NLP venues come from a handful of western
countries and (lately) China; whereas, very few papers from Africa and South
America get published. Similar disparities are also believed to exist for paper
citation counts. In the spirit of "what we do not measure, we cannot improve",
this work asks a series of questions on the relationship between geographical
location and publication success (acceptance in top NLP venues and citation
impact). We first created a dataset of 70,000 papers from the ACL Anthology,
extracted their meta-information, and generated their citation network. We then
show that not only are there substantial geographical disparities in paper
acceptance and citation but also that these disparities persist even when
controlling for a number of variables such as venue of publication and
sub-field of NLP. Further, despite some steps taken by the NLP community to
improve geographical diversity, we show that the disparity in publication
metrics across locations is still on an increasing trend since the early 2000s.
We release our code and dataset here:
https://github.com/iamjanvijay/acl-cite-net
