Statistical Script Learning with Multi-Argument Events

Experiments on a large corpus using the task of inferring held-out events and the “narrative cloze evaluation” demonstrate that modeling multi-argument events improves predictive accuracy.

Abstract

Scripts represent knowledge of stereotypical event sequences that can aid text understanding. Initial statistical methods have been developed to learn probabilistic scripts from raw text corpora; however, they utilize a very impoverished representation of events, consisting of a verb and one dependent argument. We present a script learning approach that employs events with multiple arguments. Unlike previous work, we model the interactions between multiple entities in a script. Experiments on a large corpus using the task of inferring held-out events (the "narrative cloze evaluation") demonstrate that modeling multi-argument events improves predictive accuracy.