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:
- Cambridge, MA
- Editors:
- Lillian Lee, Mark Johnson, Kristina Toutanova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 31–44
- Language:
- URL:
- https://aclanthology.org/Q17-1003
- DOI:
- 10.1162/tacl_a_00044
- Cite (ACL):
- Ashutosh Modi, Ivan Titov, Vera Demberg, Asad Sayeed, and Manfred Pinkal. 2017. Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction. Transactions of the Association for Computational Linguistics, 5:31–44.
- Cite (Informal):
- Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction (Modi et al., TACL 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/Q17-1003.pdf