Aspect-based sentiment analysis (ABSA) task consists of three typical
subtasks: aspect term extraction, opinion term extraction, and sentiment
polarity classification. These three subtasks are usually performed jointly to
save resources and reduce the error propagation in the pipeline. However, most
of the existing joint models only focus on the benefits of encoder sharing
between subtasks but ignore the difference. Therefore, we propose a joint ABSA
model, which not only enjoys the benefits of encoder sharing but also focuses
on the difference to improve the effectiveness of the model. In detail, we
introduce a dual-encoder design, in which a pair encoder especially focuses on
candidate aspect-opinion pair classification, and the original encoder keeps
attention on sequence labeling. Empirical results show that our proposed model
shows robustness and significantly outperforms the previous state-of-the-art on
four benchmark datasets.

本研究在进行方面基情感分析 (ABSA) 任务时，提出了一种双编码器的设计方法，既保留了编码器共享的优点，又针对不同的任务进行了差异化改进，经实验证明，该模型在四个基准数据集上表现显著优于之前最先进的模型。