TABLEIE: Capturing the Interactions Among Sub-Tasks in Information Extraction via Double Tables

Information Extraction mainly consists of three sub-tasks, Named Entity Recognition, Relation Extraction and Event Extraction. Although these sub-tasks are highly correlated with each other, most previous works simply focus on part of them and ignore the interactions among different sub-tasks. Recently, some graph-based models are proposed to cover all the interactions among different IE sub-tasks. However, the use of Graph Neural Network brings heavy computation burden, damaging the model efficiency. In this paper, we propose a double-table framework, TableIE, to capture the interactions among IE sub-tasks as well as improve the model efficiency. Specifically, TableIE has an entity-relation table and an event table, based on which we propose both within-table and cross-table interaction through a novel table integration technique. Such technique makes use of an information-aware mask to extract more essential information in the table during the integration, which we call discriminative interaction. Our extensive experiments demonstrate that TableIE outperforms the previous state-of-the-art up to 1.4 on the ACE05 dataset. Besides, since TableIE does not involve the time-consuming graph operation, it is also more efficient than the previous graph-based models, with 13x speed-up in the inference stage. Our code is available at https://github.com/PKUnlp-icler/TableIE