Natural Language Decompositions of Implicit Content Enable Better Text Representations
Alexander Hoyle, Rupak Sarkar, Pranav Goel, Philip Resnik
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.
BibTeX
@inproceedings{
hoyle2023natural,
title={Natural Language Decompositions of Implicit Content Enable Better Text Representations},
author={Alexander Hoyle and Rupak Sarkar and Pranav Goel and Philip Resnik},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=AoGdaivPEh}
}