  Although passivization is productive in English, it is not completely general
-- some exceptions exist (e.g. *One hour was lasted by the meeting). How do
English speakers learn these exceptions to an otherwise general pattern? Using
neural network language models as theories of acquisition, we explore the
sources of indirect evidence that a learner can leverage to learn whether a
verb can passivize. We first characterize English speakers' judgments of
exceptions to the passive, confirming that speakers find some verbs more
passivizable than others. We then show that a neural network language model can
learn restrictions to the passive that are similar to those displayed by
humans, suggesting that evidence for these exceptions is available in the
linguistic input. We test the causal role of two hypotheses for how the
language model learns these restrictions by training models on modified
training corpora, which we create by altering the existing training corpora to
remove features of the input implicated by each hypothesis. We find that while
the frequency with which a verb appears in the passive significantly affects
its passivizability, the semantics of the verb does not. This study highlight
the utility of altering a language model's training data for answering
questions where complete control over a learner's input is vital.
