@inproceedings{qi-etal-2025-generalizable,
    title = "Generalizable Cross-Lingual Cognitive Distortion Detection with Standardized Annotations and Multi-Task Learning",
    author = "Qi, Hongzhi  and
      Bai, Nan  and
      Li, Jianqiang  and
      Zhai, Wei  and
      Zhao, Qing  and
      Gao, Qi  and
      Yang, Bing Xiang  and
      Fu, Guanghui",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.826/",
    pages = "16041--16051",
    ISBN = "979-8-89176-256-5",
    abstract = "Cognitive distortion is a critical issue in psychology, with most existing studies based on Burns' cognitive distortion theory. However, differences in annotation standards lead to variations in building analysis tools, resulting in inconsistent analyses and limiting the generalizability of findings, especially in large-scale and cross-linguistic contexts. To address this issue, we collected all publicly available datasets (four in total) and conducted a series of experiments to evaluate the generalizability of various cross-linguistic models. The results indicate that models exhibit significant performance differences across datasets, highlighting the generalization problem. To mitigate this issue, we propose two solutions. First, we propose a multi-task learning model based on teacher student architecture solution, which demonstrates improved generalization performance in our experiments. Second, we introduce a new dataset ({\textasciitilde}5,000 samples) derived from reannotating existing open datasets to ensure standardized alignment. The annotation process we provided is interpretable and grounded in psychological principles. Based on this, we constructed large language models with cognitive reasoning chains, enhancing both generalizability and interpretability. This study identifies the generalization challenge in cognitive distortion research, and our experiments show that the proposed solutions significantly improve model performance. The dataset and code are publicly available at: https://github.com/HongzhiQ/CrossLinCD."
}