Work

Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction

Year: 2017

Type: article

Abstract: Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.

Source: Transactions of the Association for Computational Linguistics

DOI: 10.1162/tacl_a_00044

Authors: Ashutosh Modi, Ivan Titov, Vera Demberg, Asad Sayeed, Manfred Pinkal

Institutions: Saarland University, University of Amsterdam

Language: English

Topic: Syntax, Semantics, Linguistic Variation

Subfield: Language and Linguistics

Field: Arts and Humanities

Domain: Social Sciences

Open Access status: diamond

Funders: National Science Foundation, Deutsche Forschungsgemeinschaft, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, H2020 European Research Council

Awards: Induction of Broad-Coverage Semantic Parsers, G1951765764, Scaling Semantic Parsing to Unrestricted Domains, G893193274