MRCAug: Data Augmentation via Machine Reading Comprehension for Document-Level Event Argument Extraction

Abstract

Document-level event argument extraction (EAE) is a critical event semantic understanding task that requires a model to identify an event's global arguments beyond the sentence level. Existing approaches to this problem are based on supervised learning, which require a large amount of labeled data for model training. However, due to the complicated structure of an event, human annotation for this task is costly, and the issue of inadequacy of training data has long hampered the study. In this study, we propose a novel approach to mitigating the data sparsity problem faced by document-level EAE, by linking the task with machine reading comprehension (MRC). Particularly, we devise two data augmentation regimes via MRC, including an implicit knowledge transfer method, which enables knowledge transfer from other tasks to the document-level EAE task, and an explicit data generation method, which can explicitly generate new training examples by treating a pre-trained MRC model as an annotator. Furthermore, we propose a self-training based noise reduction strategy that can effectively addresses the out-of-domain noise introduced by the data augmentation methods. The extensive assessments on three benchmarks have validated the effectiveness of our approach — it not only achieves state-of-the-art performance but also demonstrates superior results in the data-low scenario.