  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.
