@inproceedings{wu-etal-2026-emotion,
    title = "Emotion Trajectory-aware Retrieval for {M}arkov-driven Emotion Anticipation in {LLM}-based Emotional Support Conversation",
    author = "Wu, Hongyan  and
      Tian, Zhiliang  and
      Huang, Zhen  and
      Hu, Tengyue  and
      Qiao, Linbo  and
      Gao, Yifu  and
      Liu, Feng  and
      Li, Dongsheng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.2127/",
    pages = "42887--42905",
    ISBN = "979-8-89176-395-1",
    abstract = "Emotional support conversation (ESC) aims to alleviate users' psychological stress. Selecting the appropriate strategy is crucial for effective emotional support. Current strategy planner-based methods prioritize immediate responses while neglecting users' future reactions. Some studies retrieve historical examples with similar emotions to the current utterance, then anticipating future emotions based on next-turn emotions of historical examples. However, their retrievals focus on the current emotion (i.e. a single-turn emotion state), while they ignore the evolution of user{'}s emotion before the current state. We argue that retrievals considering the whole emotional trajectories enables models to capture the dynamic emotional needs, thereby enhancing the anticipation of future emotions. To this end, we propose Markov-driven emotion anticipation framework with emotion trajectory-aware retrieval for LLM-based ESC, which anticipates future emotion states to guide strategy planning and achieve sustained emotional support. First, we construct a dynamic emotion memory and perform hierarchical retrieval that combines semantic matching and emotion trajectory alignment. Then, we model emotional transitions as Markov chains, leveraging trajectory-aware retrieval to estimate future emotion. Finally, we use the anticipated emotion to steer LLMs in generating candidate strategies and introduce active online learning to optimize the planner, boosting its robustness on diverse users. Experiments on two datasets with two models shows that our method excels all baselines."
}