Event Detection as Question Answering with Entity Information
This work addresses event detection in natural language processing, offering a novel paradigm that improves accuracy for researchers and practitioners.
The paper tackles event detection by reformulating it as a question-answering problem with entity support, achieving significant state-of-the-art performance on the ACE 2005 corpus and demonstrating the ability to extract unseen event types.
In this paper, we propose a recent and under-researched paradigm for the task of event detection (ED) by casting it as a question-answering (QA) problem with the possibility of multiple answers and the support of entities. The extraction of event triggers is, thus, transformed into the task of identifying answer spans from a context, while also focusing on the surrounding entities. The architecture is based on a pre-trained and fine-tuned language model, where the input context is augmented with entities marked at different levels, their positions, their types, and, finally, the argument roles. Experiments on the ACE~2005 corpus demonstrate that the proposed paradigm is a viable solution for the ED task and it significantly outperforms the state-of-the-art models. Moreover, we prove that our methods are also able to extract unseen event types.