An effective multi-task learning model for end-to-end emotion-cause pair extraction

Emotion-cause pair extraction (ECPE), as an extended research direction of emotion cause extraction, aims to extract emotion and its corresponding causes for a given document. Previous methods solved this problem in a two-stage fashion. Nevertheless, these methods suffered from the problem of error propagation. Moreover, there exists the problem of label imbalance for the ECPE task. In order to solve the above problems, in this paper, we propose a novel end-to-end multi-task learning model which contains a shared module and a task-specific module to simultaneously perform emotion extraction, cause extraction, and emotion-cause pair extraction. The above three tasks share the shallow sharing module, and the shared information among mining tasks is realized to achieve mutual benefit. Then each task generates task-specific features and completes the corresponding tasks in the task-specific module. In addition, we propose a sampling-based method to construct the training set for the ECPE task to alleviate the problem of label imbalance and enable our model to focus on extracting the pairs with the corresponding emotion-cause relationship. Experimental results show that our model outperforms many strong baselines with 75.48%, 75.57%, and 75.03% in P, R, and F1 score, respectively.