  When people interpret text, they rely on inferences that go beyond the
observed language itself. Inspired by this observation, we introduce a method
for the analysis of text that takes implicitly communicated content explicitly
into account. We use a large language model to produce sets of propositions
that are inferentially related to the text that has been observed, then
validate the plausibility of the generated content via human judgments.
Incorporating these explicit representations of implicit content proves useful
in multiple problem settings that involve the human interpretation of
utterances: assessing the similarity of arguments, making sense of a body of
opinion data, and modeling legislative behavior. Our results suggest that
modeling the meanings behind observed language, rather than the literal text
alone, is a valuable direction for NLP and particularly its applications to
social science.
