DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable
  Causal Inference
Jialong Wu Linhai Zhang Deyu Zhou Guoqiang Xu
Though notable progress has been made, neural-based aspect-based sentiment
analysis (ABSA) models are prone to learn spurious correlations from annotation
biases, resulting in poor robustness on adversarial data transformations. Among
the debiasing solutions, causal inference-based methods have attracted much
research attention, which can be mainly categorized into causal intervention
methods and counterfactual reasoning methods. However, most of the present
debiasing methods focus on single-variable causal inference, which is not
suitable for ABSA with two input variables (the target aspect and the review).
In this paper, we propose a novel framework based on multi-variable causal
inference for debiasing ABSA. In this framework, different types of biases are
tackled based on different causal intervention methods. For the review branch,
the bias is modeled as indirect confounding from context, where backdoor
adjustment intervention is employed for debiasing. For the aspect branch, the
bias is described as a direct correlation with labels, where counterfactual
reasoning is adopted for debiasing. Extensive experiments demonstrate the
effectiveness of the proposed method compared to various baselines on the two
widely used real-world aspect robustness test set datasets.