@inproceedings{bernardy-etal-2019-bayesian,
title = "{B}ayesian Inference Semantics: A Modelling System and A Test Suite",
author = "Bernardy, Jean-Philippe and
Blanck, Rasmus and
Chatzikyriakidis, Stergios and
Lappin, Shalom and
Maskharashvili, Aleksandre",
editor = "Mihalcea, Rada and
Shutova, Ekaterina and
Ku, Lun-Wei and
Evang, Kilian and
Poria, Soujanya",
booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-1029",
doi = "10.18653/v1/S19-1029",
pages = "263--272",
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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%0 Conference Proceedings
%T Bayesian Inference Semantics: A Modelling System and A Test Suite
%A Bernardy, Jean-Philippe
%A Blanck, Rasmus
%A Chatzikyriakidis, Stergios
%A Lappin, Shalom
%A Maskharashvili, Aleksandre
%Y Mihalcea, Rada
%Y Shutova, Ekaterina
%Y Ku, Lun-Wei
%Y Evang, Kilian
%Y Poria, Soujanya
%S Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F bernardy-etal-2019-bayesian
%X 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.
%R 10.18653/v1/S19-1029
%U https://aclanthology.org/S19-1029
%U https://doi.org/10.18653/v1/S19-1029
%P 263-272
Markdown (Informal)
[Bayesian Inference Semantics: A Modelling System and A Test Suite](https://aclanthology.org/S19-1029) (Bernardy et al., *SEM 2019)
ACL
- Jean-Philippe Bernardy, Rasmus Blanck, Stergios Chatzikyriakidis, Shalom Lappin, and Aleksandre Maskharashvili. 2019. Bayesian Inference Semantics: A Modelling System and A Test Suite. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 263–272, Minneapolis, Minnesota. Association for Computational Linguistics.