Information Value: Measuring Utterance Predictability as Distance from Plausible Alternatives
Year: 2023
Type: preprint
Abstract: We present information value, a measure which quantifies the predictability of an utterance relative to a set of plausible alternatives. We introduce a method to obtain interpretable estimates of information value using neural text generators, and exploit their psychometric predictive power to investigate the dimensions of predictability that drive human comprehension behaviour. Information value is a stronger predictor of utterance acceptability in written and spoken dialogue than aggregates of token-level surprisal and it is complementary to surprisal for predicting eye-tracked reading times.
Source: arXiv (Cornell University)
Institutions: University of Amsterdam, University of Edinburgh
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
Awards: G2388848714, G434337789
OpenAlex ID: W4387892271