Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction

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


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.
Anthology ID:
Q17-1003
Volume:
Transactions of the Association for Computational Linguistics, Volume 5
Month:
Year:
2017
Address:
Venue:
TACL
SIG:
Publisher:
Note:
Pages:
31–44
Language:
URL:
https://aclanthology.org/Q17-1003
DOI:
10.1162/tacl_a_00044
Bibkey:
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PDF:
https://aclanthology.org/Q17-1003.pdf
Video:
 https://vimeo.com/234958123