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        <title>A Compositional Bayesian Semantics for Natural Language</title>
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        <namePart type="given">Jean-Philippe</namePart>
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            <title>Proceedings of the First International Workshop on Language Cognition and Computational Models</title>
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            <namePart type="given">Manjira</namePart>
            <namePart type="family">Sinha</namePart>
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            <namePart type="given">Tirthankar</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T A Compositional Bayesian Semantics for Natural Language
%A Bernardy, Jean-Philippe
%A Blanck, Rasmus
%A Chatzikyriakidis, Stergios
%A Lappin, Shalom
%Y Sinha, Manjira
%Y Dasgupta, Tirthankar
%S Proceedings of the First International Workshop on Language Cognition and Computational Models
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F bernardy-etal-2018-compositional
%X 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.
%U https://aclanthology.org/W18-4101
%P 1-10

Markdown (Informal)

[A Compositional Bayesian Semantics for Natural Language](https://aclanthology.org/W18-4101) (Bernardy et al., LCCM 2018)

• A Compositional Bayesian Semantics for Natural Language (Bernardy et al., LCCM 2018)

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.