# Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction

arXiv: https://arxiv.org/abs/1702.03121 · HTML: https://mlsanity.com/paper/1702.03121/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2017.

## Built on by (most cited first, 6 of 6)

- [proScript: Partially Ordered Scripts Generation via Pre-trained Language Models](https://mlsanity.com/paper/2104.08251/) (2021)
- [U-CREAT: Unsupervised Case Retrieval using Events extrAcTion](https://mlsanity.com/paper/2307.05260/) (2023)
- [Drafting Event Schemas using Language Models](https://mlsanity.com/paper/2305.14847/) (2023)
- [ScriptWorld: Text Based Environment For Learning Procedural Knowledge](https://mlsanity.com/paper/2307.03906/) (2023)
- [Changing the Narrative Perspective: From Deictic to Anaphoric Point of View](https://mlsanity.com/paper/2103.04176/) (2021)
- [Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution](https://mlsanity.com/paper/2109.13105/) (2021)

## Abstract (arXiv)

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.