Linguistic interpretation as inference under argument system uncertainty: the case of epistemic must

Brandon Waldon


Abstract
Modern semantic analyses of epistemic language (incl. the modals must and might) can be characterized by the following ‘credence assumption’: speakers have full certainty regarding the propositions that structure their epistemic state. Intuitively, however: a) speakers have graded, rather than categorical, commitment to these propositions, which are often never fully and explicitly articulated; b) listeners have higher-order uncertainty about this speaker uncertainty; c) must p is used to communicate speaker commitment to some conclusion p and to indicate speaker commitment to the premises that condition the conclusion. I explore the consequences of relaxing the credence assumption by extending the argument system semantic framework first proposed by Stone (1994) to a Bayesian probabilistic framework of modeling pragmatic interpretation (Goodman and Frank, 2016). The analysis makes desirable predictions regarding the behavior and interpretation of must, and it suggests a new way of considering the nature of context and communicative exchange.
Anthology ID:
2020.pam-1.5
Volume:
Proceedings of the Probability and Meaning Conference (PaM 2020)
Month:
June
Year:
2020
Address:
Gothenburg
Editors:
Christine Howes, Stergios Chatzikyriakidis, Adam Ek, Vidya Somashekarappa
Venue:
PaM
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
34–40
Language:
URL:
https://aclanthology.org/2020.pam-1.5
DOI:
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Cite (ACL):
Brandon Waldon. 2020. Linguistic interpretation as inference under argument system uncertainty: the case of epistemic must. In Proceedings of the Probability and Meaning Conference (PaM 2020), pages 34–40, Gothenburg. Association for Computational Linguistics.
Cite (Informal):
Linguistic interpretation as inference under argument system uncertainty: the case of epistemic must (Waldon, PaM 2020)
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https://preview.aclanthology.org/nschneid-patch-4/2020.pam-1.5.pdf