  Deep learning systems frequently fail at out-of-context (OOC) prediction, the
problem of making reliable predictions on uncommon or unusual inputs or
subgroups of the training distribution. To this end, a number of benchmarks for
measuring OOC performance have recently been introduced. In this work, we
introduce a framework unifying the literature on OOC performance measurement,
and demonstrate how rich auxiliary information can be leveraged to identify
candidate sets of OOC examples in existing datasets. We present NOOCh: a suite
of naturally-occurring "challenge sets", and show how varying notions of
context can be used to probe specific OOC failure modes. Experimentally, we
explore the tradeoffs between various learning approaches on these challenge
sets and demonstrate how the choices made in designing OOC benchmarks can yield
varying conclusions.
