End-to-End Aspect-based Sentiment Analysis with Hierarchical Multi-task Learning

Abstract End-to-end aspect-based sentiment analysis(E2E-ABSA) is a sequence labeling task which detects aspect terms and the corresponding sentiment simultaneously. Previous works ignore the useful task-specific knowledge and embed the vital aspect and sentiment attributes implicitly in the intermediate layers. In this paper, we propose a hierarchical multi-task learning framework, which explicitly leverages task-related knowledge via the supervision of intermediate layers. Specifically, aspect term extraction, sentiment lexicon detection, and aspect sentiment detection are designed to encode the aspect boundary and sentiment information. The tasks are in charge of different perspectives and levels of knowledge, which provide multi-fold regulation effects to optimize the main task. Unlike vanilla multi-task learning, all the tasks are integrated into a hierarchical structure to help the higher-level tasks make full use of the lower-level tasks’ information. Experimental results on three datasets demonstrate that the proposed method achieves state-of-the-art results. Further analysis shows that the proposed method achieves better performance than single-task and vanilla multi-task learning methods and yields a more discriminative feature representation.