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An entropy-regularized counterfactual framework for robust and generalizable ABSA
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DOI：10.1007/s40747-025-02170-7.png)
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Abstract
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Pre-trained language models have demonstrated outstanding performance in aspect-based sentiment analysis (ABSA). However, existing methods are often hindered by spurious correlations between aspect-level features and sentiment labels, resulting in incorrect predictions. To address this issue, we propose a new counterfactual data generation framework based on contrastive learning and information entropy (called CLIE). CLIE primarily consists of two key components: the generative counterfactual synthesis engine (referred to as GCSE) and the entropy-driven contrastive discrimination network (referred to as EDCDN). First, GCSE selects key features by combining mutual information and normalised entropy, and uses entropy-weighted contrastive learning to construct a sample semantic topology, driving the LLM to generate high-quality counterfactual instances to reduce spurious correlations. Then, EDCDN employs information entropy for gated filtering and uses entropy-weighted contrastive learning with mutual information regularisation terms to suppress contextual bias. The filtered data is merged with the original dataset for training, thereby guiding the model to learn effective and robust representations for the ABSA task. We conducted extensive evaluations of our model on four widely used ABSA benchmark datasets, and the experimental results demonstrate that our model outperforms other SOTA models and exhibits excellent robustness.
Keywords:
Aspect-based sentiment analysis
Contrastive learning
Information entropy
Counterfactual data generation
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