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Bayesian Inference Semantics: A Modelling System and A Test Suite

Conference paper
Authors Jean-Philippe Bernardy
Rasmus Blanck
Stergios Chatzikyriakidis
Shalom Lappin
Aleksandre Maskharashvili
Published in Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM), 6-7 June 2019, Minneapolis, Minnesota, USA / Rada Mihalcea, Ekaterina Shutova, Lun-Wei Ku, Kilian Evang, Soujanya Poria (Editors)
ISBN 9781510887596
Publisher Association for Computational Linguistics
Publication year 2019
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Links https://www.aclweb.org/anthology/S1...
Keywords Bayesian models, probabilistic semantics, probabilistic programming languages, Markov Chain Monte Carlo sampling, generalised quantifiers, vague predicates, compositionality, inference
Subject categories Computational linguistics, Linguistics

Abstract

We present BIS, a Bayesian Inference Semantics, for probabilistic reasoning in natural language. The current system is based on the framework of Bernardy et al. (2018), but departs from it in important respects. BIS makes use of Bayesian learning for inferring a hypothesis from premises. This involves estimating the probability of the hypothesis, given the data supplied by the premises of an argument. It uses a syntactic parser to generate typed syntactic structures that serve as input to a model generation system. Sentences are interpreted compositionally to probabilistic programs, and the corresponding truth values are estimated using sampling methods. BIS successfully deals with various probabilistic semantic phenomena, including frequency adverbs, generalised quantifiers, generics, and vague predicates. It performs well on a number of interesting probabilistic reasoning tasks. It also sustains most classically valid inferences (instantiation, de Morgan’s laws, etc.). To test BIS we have built an experimental test suite with examples of a range of probabilistic and classical inference patterns.

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