  Previous studies investigating the syntactic abilities of deep learning
models have not targeted the relationship between the strength of the
grammatical generalization and the amount of evidence to which the model is
exposed during training. We address this issue by deploying a novel
word-learning paradigm to test BERT's few-shot learning capabilities for two
aspects of English verbs: alternations and classes of selectional preferences.
For the former, we fine-tune BERT on a single frame in a verbal-alternation
pair and ask whether the model expects the novel verb to occur in its sister
frame. For the latter, we fine-tune BERT on an incomplete selectional network
of verbal objects and ask whether it expects unattested but plausible
verb/object pairs. We find that BERT makes robust grammatical generalizations
after just one or two instances of a novel word in fine-tuning. For the verbal
alternation tests, we find that the model displays behavior that is consistent
with a transitivity bias: verbs seen few times are expected to take direct
objects, but verbs seen with direct objects are not expected to occur
intransitively.
