  Transformer-based language models have shown strong performance on an array
of natural language understanding tasks. However, the question of how these
models react to implicit meaning has been largely unexplored. We investigate
this using the complement coercion phenomenon, which involves sentences like
"The student finished the book about sailing" where the action "reading" is
implicit. We compare LMs' surprisal estimates at various critical sentence
regions in sentences with and without implicit meaning. Effects associated with
recovering implicit meaning were found at a critical region other than where
sentences minimally differ. We then use follow-up experiments to factor out
potential confounds, revealing different perspectives that offer a richer and
more accurate picture.
