  Both scientific progress and individual researcher careers depend on the
quality of peer review, which in turn depends on paper-reviewer matching.
Surprisingly, this problem has been mostly approached as an automated
recommendation problem rather than as a matter where different stakeholders
(area chairs, reviewers, authors) have accumulated experience worth taking into
account. We present the results of the first survey of the NLP community,
identifying common issues and perspectives on what factors should be considered
by paper-reviewer matching systems. This study contributes actionable
recommendations for improving future NLP conferences, and desiderata for
interpretable peer review assignments.
