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Theory-Driven Statistical Modeling for Semantics and Pragmatics: A Case Study on Grammatically Generated Implicature Readings

Published online by Cambridge University Press:  01 January 2026

Michael Franke  and
Leon Bergen
Michael Franke*
Affiliation:
University of Osnabrück
Leon Bergen*
Affiliation:
University of California, San Diego
*
[mchfranke@gmail.com]
[lbergen@ucsd.edu]
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Abstract

Computational probabilistic modeling is increasingly popular in linguistics, but its relationship with linguistic theory is ambivalent. We argue here for the potential benefit of theory-driven statistical modeling, based on a case study situated at the semantics-pragmatics interface. Using data from a novel experiment, we employ Bayesian model comparison to evaluate the predictive adequacy of four models that differ in the extent to and manner in which grammatically generated candidate readings are taken into account in four probabilistic pragmatic models of utterance and interpretation choice. The data provide strong evidence for the idea that the full range of potential readings made available by recently popular grammatical approaches to scalar-implicature computation might be needed, and that classical Gricean reasoning may help manage the manifold ambiguity introduced by grammatical approaches to these. The case study thereby shows a way of bridging linguistic theory and empirical data with the help of probabilistic pragmatic modeling as a linking function.

Keywords

pragmaticscomputational modelingscalar implicaturegrammaticalismBayesian model selection

Information

Type
Research Report
Information
Language , Volume 96 , Issue 2 , June 2020 , pp. e77 - e96
Copyright
Copyright © 2020 Linguistic Society of America

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