Event extraction as machine reading comprehension with question-context bridging

Abstract

Most existing methods regard event extraction as the classification task. They not only heavily rely on named entity recognition, causing error propagation, but are also inefficient in low resource scenarios. To deal with above challenges, we propose an improved machine reading comprehension (MRC) approach, namely MRCBEE. Firstly, a new paradigm is applied to redefine event extraction as MRC task by designing question templates for event detection and argument extraction tasks. Specially, Question-Context Bridging is a new graph structure drawn to reconstruct the semantic relationship between the template and the text, in order to strengthen the prompt role of question templates. Then, a cross-domain attention module is designed to integrate both the semantic feature and global feature of words. A new GNN based on gated mechanism is proposed to capture the global feature and filter the information of invalid neighbors. Finally, the results of our experiments show that MRCBEE achieves better performance than the state-of-the-art methods on ACE2005 and ERE datasets. And it outperforms prior methods in low resource and complex text scenarios.