  As machine translation (MT) systems progress at a rapid pace, questions of
their adequacy linger. In this study we focus on negation, a universal, core
property of human language that significantly affects the semantics of an
utterance. We investigate whether translating negation is an issue for modern
MT systems using 17 translation directions as test bed. Through thorough
analysis, we find that indeed the presence of negation can significantly impact
downstream quality, in some cases resulting in quality reductions of more than
60%. We also provide a linguistically motivated analysis that directly explains
the majority of our findings. We release our annotations and code to replicate
our analysis here: https://github.com/mosharafhossain/negation-mt.
