Existing works for aspect-based sentiment analysis (ABSA) have adopted a unified approach, which allows the interactive relations among subtasks. However, we observe that these methods tend to predict polarities based on the literal meaning of aspect and opinion terms and mainly consider relations implicitly among subtasks at the word level. In addition, identifying multiple aspect-opinion pairs with their polarities is much more challenging. Therefore, a comprehensive understanding of contextual information w.r.t. the aspect and opinion are further required in ABSA. In this paper, we propose Deep Contextualized Relation-Aware Network (DCRAN), which allows interactive relations among subtasks with deep contextual information based on two modules (i.e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies). Especially, we design novel self-supervised strategies for ABSA, which have strengths in dealing with multiple aspects. Experimental results show that DCRAN significantly outperforms previous state-of-the-art methods by large margins on three widely used benchmarks.

本文提出了一种深度上下文感知关系网络（DCRAN）方法，基于两个模块（即方面和观点传播、显式自监督策略），允许任务之间的交互关系和深度上下文信息，并设计了一些新的对于ABSA有很好应用的自监督策略，实验表明，在三种广泛使用的基准测试中，DCRAN的性能明显优于以往的最先进方法。

基于深度上下文和关系感知的方面情感分析学习