  Event coreference models cluster event mentions pertaining to the same
real-world event. Recent models rely on contextualized representations to
recognize coreference among lexically or contextually similar mentions.
However, models typically fail to leverage commonsense inferences, which is
particularly limiting for resolving lexically-divergent mentions. We propose a
model that extends event mentions with temporal commonsense inferences. Given a
complex sentence with multiple events, e.g., "The man killed his wife and got
arrested", with the target event "arrested", our model generates plausible
events that happen before the target event - such as "the police arrived", and
after it, such as "he was sentenced". We show that incorporating such
inferences into an existing event coreference model improves its performance,
and we analyze the coreferences in which such temporal knowledge is required.
