Bayesian Inference Semantics: A Modelling System and A Test Suite

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.

When redundancy is useful: A Bayesian approach to “overinformative” referring expressions.

“…This idea has its oldest modern precursor in fuzzy logic (Zadeh, 1965). It is similar in spirit to recently proposed models of meaning in both computational semantics, which assign probabilities rather than truth conditions to sentences (Bernardy, Blanck, Chatzikyriakidis, & Lappin, 2018), and in natural language processing, which treat word and sentence meanings as vectors of real numbers (Devlin, Chang, Lee, & Toutanova, 2018; Pennington, Socher, & Manning, 2014; Peters et al, 2018).…”

“…That is, rather than assuming that an object is unambiguously big (or not) or unambiguously blue (or not), this continuous semantics captures that objects count as big or blue to varying degrees (similar to approaches in fuzzy logic, prototype theory, and recent developments in natural language processing; Bernardy et al, 2018; Rosch, 1973; Zadeh, 1965).…”

Unitary Recurrent Networks: Algebraic and Linear Structures for Syntax

“…Montague left the type of individuals abstract, but it can be further instantiated to vector spaces, with various interpretations. (Bernardy, Blanck, et al, 2018;Emerson andCopestake, 2016, 2017;Grefenstette et al, 2011;Grove and Bernardy, 2021) 11.2.2 Sequence algebras and parsing On Montague's view, semantics is based on syntactic structure. However, languages do not come with labelled syntactic structures.…”

Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics

“…Functional Distributional Semantics is related to other probabilistic semantic approaches. Goodman and Lassiter (2015) and Bernardy et al (2018Bernardy et al ( , 2019 represent meaning as a probabilistic program. This paper brings Functional Distributional Semantics closer to their work, because a probabilistic scope tree can be seen as a probabilistic program.…”