  New models for natural language understanding have recently made an
unparalleled amount of progress, which has led some researchers to suggest that
the models induce universal text representations. However, current benchmarks
are predominantly targeting semantic phenomena; we make the case that
pragmatics needs to take center stage in the evaluation of natural language
understanding. We introduce PragmEval, a new benchmark for the evaluation of
natural language understanding, that unites 11 pragmatics-focused evaluation
datasets for English. PragmEval can be used as supplementary training data in a
multi-task learning setup, and is publicly available, alongside the code for
gathering and preprocessing the datasets. Using our evaluation suite, we show
that natural language inference, a widely used pretraining task, does not
result in genuinely universal representations, which presents a new challenge
for multi-task learning.
