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

Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs