Causal Inference of Script Knowledge
This work addresses script induction for natural language processing, offering a novel causal perspective that improves over incremental correlation-based techniques.
The paper tackled the problem of inducing script knowledge from text by moving beyond correlation-based methods to a causal inference approach, showing that their method better matches human intuition in evaluations.
When does a sequence of events define an everyday scenario and how can this knowledge be induced from text? Prior works in inducing such scripts have relied on, in one form or another, measures of correlation between instances of events in a corpus. We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions. Through both human and automatic evaluations, we show that the output of our method based on causal effects better matches the intuition of what a script represents