What happens before and after: Multi-Event Commonsense in Event Coreference Resolution

TL;DR

This paper introduces a model that enhances event coreference resolution by incorporating temporal commonsense inferences, improving the recognition of coreferent events especially when mentions are lexically divergent.

Contribution

The paper presents a novel approach that extends event mentions with temporal commonsense inferences, boosting coreference resolution performance in complex sentences.

Findings

Incorporating temporal commonsense improves coreference accuracy.

The model effectively generates plausible before-and-after events.

Temporal knowledge is crucial for resolving lexically-divergent mentions.

Abstract

Event coreference models cluster event mentions pertaining to the same real-world event. Recent models rely on contextualized representations to recognize coreference among lexically or contextually similar mentions. However, models typically fail to leverage commonsense inferences, which is particularly limiting for resolving lexically-divergent mentions. We propose a model that extends event mentions with temporal commonsense inferences. Given a complex sentence with multiple events, e.g., "The man killed his wife and got arrested", with the target event "arrested", our model generates plausible events that happen before the target event - such as "the police arrived", and after it, such as "he was sentenced". We show that incorporating such inferences into an existing event coreference model improves its performance, and we analyze the coreferences in which such temporal knowledge is required.