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        <title>From compositional semantics to Bayesian pragmatics via logical inference</title>
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    <abstract>Formal semantics in the Montagovian tradition provides precise meaning characterisations, but usually without a formal theory of the pragmatics of contextual parameters and their sensitivity to background knowledge. Meanwhile, formal pragmatic theories make explicit predictions about meaning in context, but generally without a well-defined compositional semantics. We propose a combined framework for the semantic and pragmatic interpretation of sentences in the face of probabilistic knowledge. We do so by (1) extending a Montagovian interpretation scheme to generate a distribution over possible meanings, and (2) generating a posterior for this distribution using a variant of the Rational Speech Act (RSA) models, but generalised to arbitrary propositions. These aspects of our framework are tied together by evaluating entailment under probabilistic uncertainty. We apply our model to anaphora resolution and show that it provides expected biases under suitable assumptions about the distributions of lexical and world-knowledge. Further, we observe that the model’s output is robust to variations in its parameters within reasonable ranges.</abstract>
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%0 Conference Proceedings
%T From compositional semantics to Bayesian pragmatics via logical inference
%A Grove, Julian
%A Bernardy, Jean-Philippe
%A Chatzikyriakidis, Stergios
%Y Kalouli, Aikaterini-Lida
%Y Moss, Lawrence S.
%S Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Groningen, the Netherlands (online)
%F grove-etal-2021-compositional
%X Formal semantics in the Montagovian tradition provides precise meaning characterisations, but usually without a formal theory of the pragmatics of contextual parameters and their sensitivity to background knowledge. Meanwhile, formal pragmatic theories make explicit predictions about meaning in context, but generally without a well-defined compositional semantics. We propose a combined framework for the semantic and pragmatic interpretation of sentences in the face of probabilistic knowledge. We do so by (1) extending a Montagovian interpretation scheme to generate a distribution over possible meanings, and (2) generating a posterior for this distribution using a variant of the Rational Speech Act (RSA) models, but generalised to arbitrary propositions. These aspects of our framework are tied together by evaluating entailment under probabilistic uncertainty. We apply our model to anaphora resolution and show that it provides expected biases under suitable assumptions about the distributions of lexical and world-knowledge. Further, we observe that the model’s output is robust to variations in its parameters within reasonable ranges.
%U https://aclanthology.org/2021.naloma-1.8
%P 60-70

Markdown (Informal)

[From compositional semantics to Bayesian pragmatics via logical inference](https://aclanthology.org/2021.naloma-1.8) (Grove et al., NALOMA 2021)

• From compositional semantics to Bayesian pragmatics via logical inference (Grove et al., NALOMA 2021)

ACL

• Julian Grove, Jean-Philippe Bernardy, and Stergios Chatzikyriakidis. 2021. From compositional semantics to Bayesian pragmatics via logical inference. In Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA), pages 60–70, Groningen, the Netherlands (online). Association for Computational Linguistics.