# Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction

## TL;DR

This paper develops a computational model to predict discourse referents using linguistic and script knowledge, demonstrating that script knowledge enhances prediction accuracy, but finds no evidence linking predictability to referring expression type.

## Contribution

It introduces a model combining linguistic and script knowledge for referent prediction and empirically shows the impact of script knowledge on prediction accuracy.

## Key findings

- Script knowledge significantly improves referent prediction.
- No evidence found that predictability influences referring expression type.

## 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.