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When people interpret text, they rely on inferences that go beyond theobserved language itself. Inspired by this observation, we introduce a methodfor the analysis of text that takes implicitly communicated content explicitlyinto account. We use a large language model to produce sets of propositionsthat are inferentially related to the text that has been observed, thenvalidate the plausibility of the generated content via human judgments.Incorporating these explicit representations of implicit content proves usefulin multiple problem settings that involve the human interpretation ofutterances: assessing the similarity of arguments, making sense of a body ofopinion data, and modeling legislative behavior. Our results suggest thatmodeling the meanings behind observed language, rather than the literal textalone, is a valuable direction for NLP and particularly its applications tosocial science.

Natural Language Decompositions of Implicit Content Enable Better Text Representations