DINER：基于多变量因果推断的方面级情感分析去偏方法
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference
AI总结：
本文提出DINER框架，通过多变量因果推断分别对评论分支采用后门调整、对方面分支采用反事实推理来消除ABSA模型中的标注偏差，并在两个鲁棒性测试集上验证了其有效性。
摘要：
尽管基于神经网络的方面级情感分析（ABSA）模型已取得显著进展，但它们容易从标注偏差中学习到虚假相关性，导致在对抗性数据变换上的鲁棒性较差。在各类去偏方案中，基于因果推断的方法引起了广泛的研究关注，这些方法主要可分为因果干预方法和反事实推理方法。然而，现有的大多数去偏方法都聚焦于单变量因果推断，这不适用于具有两个输入变量（目标方面和评论）的ABSA任务。本文提出了一种基于多变量因果推断的新型去偏框架。在该框架中，不同类型的偏差通过不同的因果干预方法加以处理。对于评论分支，偏差被建模为来自上下文的间接混杂因素，采用后门调整干预进行去偏；对于方面分支，偏差被描述为与标签的直接相关性，采用反事实推理进行去偏。在两个广泛使用的真实世界方面鲁棒性测试集数据集上，大量实验证明了所提方法与各种基线相比的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
