@inproceedings{yang-etal-2026-empathy,
    title = "Empathy in Diversity: Personalized Depression and Anxiety Therapy via Dialogue State Tracking and Patient-Aware Planning",
    author = "Yang, Xinwei  and
      Fan, Junyi  and
      Liu, Yuqing  and
      Wang, Jiaxuan  and
      Zhang, Jiashuai  and
      Liang, Hongru  and
      Lei, Wenqiang  and
      Song, Yao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.424/",
    pages = "9370--9416",
    ISBN = "979-8-89176-390-6",
    abstract = "Large language model (LLM) dialogue agents are increasingly used in psychological therapy, yet robustness across diverse patients remains underexplored. We address this gap with three contributions: (1) MindEval, a realistic role-play protocol for evaluating therapeutic dialogue agents; (2) MindData, a de-identified, expert-annotated corpus of therapist{--}patient dialogues (2,573 sessions; 63,348 turns); and (3) MindApt, a framework that integrates a therapeutic dialogue state tracking paradigm with a patient-aware strategic planning module. On MindEval, MindApt outperforms strong baselines on therapeutic outcomes and dialogue quality while improving conversational efficiency. To evaluate utility beyond role-play, we conducted a clinical study with real patients, demonstrating that MindApt-guided care achieves outcomes comparable to therapist-determined care, while the hybrid setting combining therapist judgment with MindApt{'}s recommendations yields the strongest overall outcomes."
}