  Recent efforts in natural language processing (NLP) commonsense reasoning
research have yielded a considerable number of new datasets and benchmarks.
However, most of these datasets formulate commonsense reasoning challenges in
artificial scenarios that are not reflective of the tasks which real-world NLP
systems are designed to solve. In this work, we present CRoW, a
manually-curated, multi-task benchmark that evaluates the ability of models to
apply commonsense reasoning in the context of six real-world NLP tasks. CRoW is
constructed using a multi-stage data collection pipeline that rewrites examples
from existing datasets using commonsense-violating perturbations. We use CRoW
to study how NLP systems perform across different dimensions of commonsense
knowledge, such as physical, temporal, and social reasoning. We find a
significant performance gap when NLP systems are evaluated on CRoW compared to
humans, showcasing that commonsense reasoning is far from being solved in
real-world task settings. We make our dataset and leaderboard available to the
research community at https://github.com/mismayil/crow.
