  Though state-of-the-art sentence representation models can perform tasks
requiring significant knowledge of grammar, it is an open question how best to
evaluate their grammatical knowledge. We explore five experimental methods
inspired by prior work evaluating pretrained sentence representation models. We
use a single linguistic phenomenon, negative polarity item (NPI) licensing in
English, as a case study for our experiments. NPIs like "any" are grammatical
only if they appear in a licensing environment like negation ("Sue doesn't have
any cats" vs. "Sue has any cats"). This phenomenon is challenging because of
the variety of NPI licensing environments that exist. We introduce an
artificially generated dataset that manipulates key features of NPI licensing
for the experiments. We find that BERT has significant knowledge of these
features, but its success varies widely across different experimental methods.
We conclude that a variety of methods is necessary to reveal all relevant
aspects of a model's grammatical knowledge in a given domain.
