@inproceedings{liu-etal-2025-dual-path,
    title = "Dual-Path Counterfactual Integration for Multimodal Aspect-Based Sentiment Classification",
    author = "Liu, Rui  and
      Cao, Jiahao  and
      Ren, Jiaqian  and
      Bai, Xu  and
      Cao, Yanan",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.1158/",
    doi = "10.18653/v1/2025.emnlp-main.1158",
    pages = "22759--22769",
    ISBN = "979-8-89176-332-6",
    abstract = "Multimodal aspect-based sentiment classification (MABSC) requires fine-grained reasoning over both textual and visual content to infer sentiments toward specific aspects. However, existing methods often rely on superficial correlations{---}particularly between aspect terms and sentiment labels{---}leading to poor generalization and vulnerability to spurious cues. To address this limitation, we propose DPCI, a novel Dual-Path Counterfactual Integration framework that enhances model robustness by explicitly modeling counterfactual reasoning in multimodal contexts. Specifically, we design a dual counterfactual generation module that simulates two types of interventions: replacing aspect terms and rewriting descriptive content, thereby disentangling the spurious dependencies from causal sentiment cues. We further introduce a sample-aware counterfactual selection strategy to retain high-quality, diverse counterfactuals tailored to each generation path. Finally, a confidence-guided integration mechanism adaptively fuses counterfactual signals into the main prediction stream. Extensive experiments on standard MABSC benchmarks demonstrate that DPCI not only achieves state-of-the-art performance but also significantly improves model robustness."
}