Event Extraction as Question Generation and Answering

TL;DR

This paper introduces QGA-EE, a novel question generation approach for event extraction that leverages contextual information and dynamic templates, outperforming previous models on the ACE05 dataset.

Contribution

It presents a new method that improves event extraction by generating context-aware questions with dynamic templates, reducing error propagation.

Findings

QGA-EE outperforms prior models on ACE05 dataset.

Dynamic templates enhance question generation quality.

Contextual question generation improves event argument extraction.

Abstract

Recent work on Event Extraction has reframed the task as Question Answering (QA), with promising results. The advantage of this approach is that it addresses the error propagation issue found in traditional token-based classification approaches by directly predicting event arguments without extracting candidates first. However, the questions are typically based on fixed templates and they rarely leverage contextual information such as relevant arguments. In addition, prior QA-based approaches have difficulty handling cases where there are multiple arguments for the same role. In this paper, we propose QGA-EE, which enables a Question Generation (QG) model to generate questions that incorporate rich contextual information instead of using fixed templates. We also propose dynamic templates to assist the training of QG model. Experiments show that QGA-EE outperforms all prior single-task-based models on the ACE05 English dataset.