Problem
Research questions and friction points this paper is trying to address.
Analyzing implicit content in text
Improving text representation models
Enhancing NLP for social science applications
Natural Language Decompositions of Implicit Content Enable Better Text Representations
🤖 AI Summary
This paper addresses the insufficient modeling of textual implicit semantics by proposing a novel paradigm that explicitly transforms “subtext” into a verifiable set of propositions. Methodologically, it systematically leverages large language models to generate implicit inference propositions from text, which are then validated for plausibility by human annotators; the resulting proposition semantics are subsequently fused into the original text representations. Key contributions include: (1) introducing the first annotated framework for implicit propositions tailored to social science tasks; and (2) demonstrating that this explicit modeling significantly outperforms literal-only representations across three distinct tasks—argument similarity assessment, public opinion interpretation, and legislative behavior simulation—with average improvements of 12.7% in F1 or accuracy. Results substantiate the effectiveness and generalizability of structured implicit semantic modeling for enhancing human-like semantic understanding.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Explicitly models implicit content
Uses large language models
Validates with human judgments
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