Event causality identification via structure optimization and reinforcement learning

Abstract

Event causality identification (ECI) aims to identify possible causal relationships between event-mention pairs in a text. In the past, ECI models mainly used classification frameworks and rarely used generative models to solve this task. Although some progress has been made, the existing approaches suffer from the following two problems: (1) In the generative approach of inter-event mention dependency paths, noise and unnecessary sentence components cannot be effectively reduced, thus limiting the ability of the model to capture the critical correlation knowledge between event mentions; and (2) Existing multi-task generative model training which uses the REINFORCE algorithm suffers from a high-variance problem that imposes limitations on capturing critical causal knowledge. Therefore, we propose a novel Structural Optimization strategy Reinforcement Learning algorithm Generation model, GenSORL. The model aims to generate causal relationships from input sentences and includes dependency path generation as a complementary task to improve the causal label prediction performance. Specifically, this approach utilizes a new dependency syntax strategy to optimize dependency-path generation and extract important ECI contextual words between event mentions. Regarding the high-variance problem, a policy gradient with baseline is proposed for training the generative model, further adopting an innovative reward function to measure the accuracy of causal prediction and generation quality. In experiments using two frequently used benchmark datasets, the proposed method outperformed state-of-the-art models.