@inproceedings{shen-etal-2025-zero,
    title = "Zero-Shot Cross-Domain Aspect-Based Sentiment Analysis via Domain-Contextualized Chain-of-Thought Reasoning",
    author = "Shen, Chuming  and
      Wei, Wei  and
      Wang, Dong  and
      Wang, Zhong-Hao",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.245/",
    doi = "10.18653/v1/2025.findings-emnlp.245",
    pages = "4558--4573",
    ISBN = "979-8-89176-335-7",
    abstract = "Cross-domain aspect-based sentiment analysis (ABSA) aims at learning specific knowledge from a source domain to perform various ABSA tasks on a target domain. Recent works mainly focus on how to use domain adaptation techniques to transfer the domain-agnostic features from the labeled source domain to the unlabeled target domain. However, it would be unwise to manually collect a large number of unlabeled data from the target domain, where such data may not be available owing to the facts like data security concerns in banking or insurance. To alleviate this issue, we propose ZeroABSA, a unified zero-shot learning framework for cross-domain ABSA that effectively eliminates dependency on target-domain annotations. Specifically, ZeroABSA consists of two novel components, namely, (1) A hybrid data augmentation module leverages large language models (LLMs) to synthesize high-quality, domain-adaptive target-domain data, by evaluating the generated samples across vocabulary richness, semantic coherence and sentiment/domain consistency, followed by iterative refinement; (2) A domain-aware chain-of-thought (COT) prompting strategy trains models on augmented data while explicitly modeling domain-invariant reasoning to bridge the well-known cross-domain gap. Extensive evaluations across four diverse domains demonstrate that ZeroABSA surpasses the-state-of-the-arts, which effectively advances the practicality of cross-domain ABSA in real-world scenarios where labeled target-domain data is unavailable."
}