  Recent work on evaluating grammatical knowledge in pretrained sentence
encoders gives a fine-grained view of a small number of phenomena. We introduce
a new analysis dataset that also has broad coverage of linguistic phenomena. We
annotate the development set of the Corpus of Linguistic Acceptability (CoLA;
Warstadt et al., 2018) for the presence of 13 classes of syntactic phenomena
including various forms of argument alternations, movement, and modification.
We use this analysis set to investigate the grammatical knowledge of three
pretrained encoders: BERT (Devlin et al., 2018), GPT (Radford et al., 2018),
and the BiLSTM baseline from Warstadt et al. We find that these models have a
strong command of complex or non-canonical argument structures like
ditransitives (Sue gave Dan a book) and passives (The book was read). Sentences
with long distance dependencies like questions (What do you think I ate?)
challenge all models, but for these, BERT and GPT have a distinct advantage
over the baseline. We conclude that recent sentence encoders, despite showing
near-human performance on acceptability classification overall, still fail to
make fine-grained grammaticality distinctions for many complex syntactic
structures.
