Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.

方面类别检测是基于方面的情感分析的一个具有挑战性的子任务，它将评论句子分类为一组预定义的方面类别。大多数现有方法将方面类别检测视为平面分类问题。然而，方面类别是相互关联的，并且它们通常以层次树结构来组织。为了利用结构信息，本文提出了一种分层多标签分类模型来检测方面类别，并使用图增强变压器网络将标签依赖信息集成到预测特征中。在四个广泛使用的基准数据集上进行了实验，表明所提出的模型优于所有强基线。