  Pretrained language models (PLMs) have achieved superhuman performance on
many benchmarks, creating a need for harder tasks. We introduce CoDA21 (Context
Definition Alignment), a challenging benchmark that measures natural language
understanding (NLU) capabilities of PLMs: Given a definition and a context each
for k words, but not the words themselves, the task is to align the k
definitions with the k contexts. CoDA21 requires a deep understanding of
contexts and definitions, including complex inference and world knowledge. We
find that there is a large gap between human and PLM performance, suggesting
that CoDA21 measures an aspect of NLU that is not sufficiently covered in
existing benchmarks.
