qiuhuachuan/PsyDial-D4

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The declared licence (apache-2.0) is classified as permissive.

Declared licence: apache-2.0, from the tagEvidence: README.md at 0ceef5a
Licence files: No licence file at the top level of the repositoryEvidence: file list at 0ceef5a
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Citation text from the card

@inproceedings{qiu-lan-2025-psydial,
    title = "{P}sy{D}ial: A Large-scale Long-term Conversational Dataset for Mental Health Support",
    author = "Qiu, Huachuan  and
      Lan, Zhenzhong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.1049/",
    doi = "10.18653/v1/2025.acl-long.1049",
    pages = "21624--21655",
    ISBN = "979-8-89176-251-0",
    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

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dataset_licence({ "id": "qiuhuachuan/PsyDial-D4" })
signal: "permissive"
stated_licence: "apache-2.0"
sources: 0