DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference

arXiv:2403.01166v1 Announce Type: new
Abstract: 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 …

abstract adversarial analysis annotation arxiv attention biases causal inference correlations cs.ai cs.cl data inference learn progress research robustness sentiment sentiment analysis solutions type