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.
@article{arxiv.1702.03121,
title = {Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction},
author = {Ashutosh Modi and Ivan Titov and Vera Demberg and Asad Sayeed and Manfred Pinkal},
journal= {arXiv preprint arXiv:1702.03121},
year = {2017}
}
Comments
14 pages, published at TACL, 2017, Volume-5, Pg 31-44, 2017