Bayesian Inference Semantics: A Modelling System and A Test Suite
Jean-Philippe
Bernardy
author
Rasmus
Blanck
author
Stergios
Chatzikyriakidis
author
Shalom
Lappin
author
Aleksandre
Maskharashvili
author
2019-06
text
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
Rada
Mihalcea
editor
Ekaterina
Shutova
editor
Lun-Wei
Ku
editor
Kilian
Evang
editor
Soujanya
Poria
editor
Association for Computational Linguistics
Minneapolis, Minnesota
conference publication
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.
bernardy-etal-2019-bayesian
10.18653/v1/S19-1029
https://aclanthology.org/S19-1029
2019-06
263
272