Structural Dependency Self-attention Based Hierarchical Event Model for Chinese Financial Event Extraction

Abstract

Document-level event extraction (DEE) now draws a huge amount of researchers’ attention. Not only the researches on sentence-level event extraction have obtained a great progress, but researchers realize that an event is usually described by multiple sentences in a document especially for fields such as finance, medicine, and judicature. Several document-level event extraction models are proposed to solve this task and obtain improvements on DEE task in recent years. However, we noticed that these models fail to exploit the entity dependency information of trigger and arguments, which ignore the dependency information between arguments, and between the trigger and arguments especially for financial domain. For DEE task, a model needs to extract the event-related entities, i.e., trigger and arguments, and predicts its corresponding roles. Thus, the entity dependency information between trigger and argument, and between arguments are essential. In this work, we define 8 types of structural dependencies and propose a document-level Chinese financial event extraction model called SSA-HEE, which explicitly explores the structure dependency information of candidate entities and improves the model’s ability to identify the relevance of entities. The experimental results show the effectiveness of the proposed model.