A Compositional Bayesian Semantics for Natural Language

Jean-Philippe Bernardy, Rasmus Blanck, Stergios Chatzikyriakidis, Shalom Lappin


Abstract
We propose a compositional Bayesian semantics that interprets declarative sentences in a natural language by assigning them probability conditions. These are conditional probabilities that estimate the likelihood that a competent speaker would endorse an assertion, given certain hypotheses. Our semantics is implemented in a functional programming language. It estimates the marginal probability of a sentence through Markov Chain Monte Carlo (MCMC) sampling of objects in vector space models satisfying specified hypotheses. We apply our semantics to examples with several predicates and generalised quantifiers, including higher-order quantifiers. It captures the vagueness of predication (both gradable and non-gradable), without positing a precise boundary for classifier application. We present a basic account of semantic learning based on our semantic system. We compare our proposal to other current theories of probabilistic semantics, and we show that it offers several important advantages over these accounts.
Anthology ID:
W18-4101
Volume:
Proceedings of the First International Workshop on Language Cognition and Computational Models
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Manjira Sinha, Tirthankar Dasgupta
Venue:
LCCM
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–10
Language:
URL:
https://aclanthology.org/W18-4101
DOI:
Bibkey:
Cite (ACL):
Jean-Philippe Bernardy, Rasmus Blanck, Stergios Chatzikyriakidis, and Shalom Lappin. 2018. A Compositional Bayesian Semantics for Natural Language. In Proceedings of the First International Workshop on Language Cognition and Computational Models, pages 1–10, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
A Compositional Bayesian Semantics for Natural Language (Bernardy et al., LCCM 2018)
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PDF:
https://aclanthology.org/W18-4101.pdf