Causality in Hundreds of Narratives of the Same Events

Abstract

Empirical research supporting computational models of narrative is often constrained by the lack of large-scale corpora with deep annotation. In this paper, we report on our annotation and analysis of a dataset of 283 individual narrations of the events in two short video clips. The utterances in the narrative transcripts were annotated to align with known events in the source videos, offering a unique opportunity to study the regularities and variations in the way that different people describe the exact same set of events. We identified the causal relationships between events in the two video clips, and investigated the role that causality plays in determining whether subjects will mention a particular story event and the likelihood that these events will be told in the order that they occurred in the original videos.