  In this paper, we focus on the imbalance issue, which is rarely studied in
aspect term extraction and aspect sentiment classification when regarding them
as sequence labeling tasks. Besides, previous works usually ignore the
interaction between aspect terms when labeling polarities. We propose a
GRadient hArmonized and CascadEd labeling model (GRACE) to solve these
problems. Specifically, a cascaded labeling module is developed to enhance the
interchange between aspect terms and improve the attention of sentiment tokens
when labeling sentiment polarities. The polarities sequence is designed to
depend on the generated aspect terms labels. To alleviate the imbalance issue,
we extend the gradient harmonized mechanism used in object detection to the
aspect-based sentiment analysis by adjusting the weight of each label
dynamically. The proposed GRACE adopts a post-pretraining BERT as its backbone.
Experimental results demonstrate that the proposed model achieves consistency
improvement on multiple benchmark datasets and generates state-of-the-art
results.
