{
 "id": "qiuhuachuan/PsyDial-D4",
 "owner": "qiuhuachuan",
 "name": "PsyDial-D4",
 "downloads": 146,
 "likes": 1,
 "last_modified": "2025-09-05T02:55:18.000Z",
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 "citation": "@inproceedings{qiu-lan-2025-psydial,\n    title = \"{P}sy{D}ial: A Large-scale Long-term Conversational Dataset for Mental Health Support\",\n    author = \"Qiu, Huachuan  and\n      Lan, Zhenzhong\",\n    editor = \"Che, Wanxiang  and\n      Nabende, Joyce  and\n      Shutova, Ekaterina  and\n      Pilehvar, Mohammad Taher\",\n    booktitle = \"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = jul,\n    year = \"2025\",\n    address = \"Vienna, Austria\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.acl-long.1049/\",\n    doi = \"10.18653/v1/2025.acl-long.1049\",\n    pages = \"21624--21655\",\n    ISBN = \"979-8-89176-251-0\",\n    abstract = \"Dialogue systems for mental health counseling aim to alleviate client distress and assist individuals in navigating personal challenges. Developing effective conversational agents for psychotherapy requires access to high-quality, real-world, long-term client-counselor interaction data, which is difficult to obtain due to privacy concerns. Although removing personally identifiable information is feasible, this process is labor-intensive. To address these challenges, we propose a novel privacy-preserving data reconstruction method that reconstructs real-world client-counselor dialogues while mitigating privacy concerns. We apply the RMRR (Retrieve, Mask, Reconstruct, Refine) method, which facilitates the creation of the privacy-preservi",
 "signal": "permissive",
 "reason": "The declared licence (apache-2.0) is classified as permissive.",
 "revision": "0ceef5ac19fb8174baa6be31ce1b2637711cb4c2",
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