  The issue of shortcut learning is widely known in NLP and has been an
important research focus in recent years. Unintended correlations in the data
enable models to easily solve tasks that were meant to exhibit advanced
language understanding and reasoning capabilities. In this survey paper, we
focus on the field of machine reading comprehension (MRC), an important task
for showcasing high-level language understanding that also suffers from a range
of shortcuts. We summarize the available techniques for measuring and
mitigating shortcuts and conclude with suggestions for further progress in
shortcut research. Importantly, we highlight two concerns for shortcut
mitigation in MRC: (1) the lack of public challenge sets, a necessary component
for effective and reusable evaluation, and (2) the lack of certain mitigation
techniques that are prominent in other areas.
