Script Induction as Language Modeling

Rachel Rudinger, Pushpendre Rastogi, Francis Ferraro, Benjamin Van Durme

Abstract: The narrative cloze is an evaluation metric commonly used for work on automatic script induction. While prior work in this area has focused on count-based methods from distributional semantics, such as pointwise mutual information, we argue that the narrative cloze can be productively reframed as a language modeling task. By training a discriminative language model for this task, we attain improvements of up to 27 percent over prior methods on standard narrative cloze metrics.