LAUSR

Event extraction aims to present unstructured text containing event information in a structured form to help people quickly mine the target information. Most of the traditional event extraction methods focus on the design of complex neural network models, which rely on a large amount of annotated data to train the models. In recent years, some researchers have proposed the use of machine reading comprehension models for event extraction; however, the existing methods are limited to the single-round question-and-answer model, ignoring the dependency relation between the elements of event arguments. In addition, the existing methods do not fully utilize knowledge such as a priori information. To address these shortcomings, a multi-round Q&A framework is proposed for event extraction, which extends the existing methods in two aspects: first, by constructing a multi-round extraction problem framework, the model can effectively exploit the hierarchical dependencies among the argument elements; second, the question-and-answer framework is populated with historical answer information encoding slots, which are integrated into the multi-round Q&A process to assist in inference. Finally, experimental results on a publicly available dataset show that the proposed model achieves superior results compared to existing methods.