What the Role is vs. What Plays the Role: Semi-Supervised Event Argument Extraction via Dual Question Answering

Event argument extraction is an essential task in event extraction, and become particularly challenging in the case of low-resource scenarios. We solve the issues in existing studies under low-resource situations from two sides. From the perspective of the model, the existing methods always suffer from the concern of insufficient parameter sharing and do not consider the semantics of roles, which is not conducive to dealing with sparse data. And from the perspective of the data, most existing methods focus on data generation and data augmentation. However, these methods rely heavily on external resources, which is more laborious to create than obtain unlabeled data. In this paper, we propose DualQA, a novel framework, which models the event argument extraction task as question answering to alleviate the problem of data sparseness and leverage the duality of event argument recognition which is to ask "What plays the role", as well as event role recognition which is to ask "What the role is", to mutually improve each other. Experimental results on two datasets prove the effectiveness of our approach, especially in extremely low-resource situations.