  A central question in natural language understanding (NLU) research is
whether high performance demonstrates the models' strong reasoning
capabilities. We present an extensive series of controlled experiments where
pre-trained language models are exposed to data that have undergone specific
corruption transformations. These involve removing instances of specific word
classes and often lead to non-sensical sentences. Our results show that
performance remains high on most GLUE tasks when the models are fine-tuned or
tested on corrupted data, suggesting that they leverage other cues for
prediction even in non-sensical contexts. Our proposed data transformations can
be used to assess the extent to which a specific dataset constitutes a proper
testbed for evaluating models' language understanding capabilities.
