@inproceedings{hryhoryeva-etal-2026-ukppsycontrol,
    title = "{UKPP}sycontrol at {S}em{E}val-2026 Task 2: Modeling Valence and Arousal Dynamics from Text",
    author = "Hryhoryeva, Darya  and
      Zurinaga, Amaia  and
      Jamalabadi, Hamidreza  and
      Gurevych, Iryna",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.76/",
    pages = "528--539",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents our system developed for SemEval-2026 Task 2. The task requires modeling both current affect and short-term affective change in chronologically ordered user-generated texts. We explore three complementary approaches: (1) LLM prompting under user-aware and user-agnostic settings, (2) a pairwise Maximum Entropy (MaxEnt) model with Ising-style interactions for structured transition modeling, and (3) a lightweight neural regression model incorporating recent affective trajectories and trainable user embeddings. Our findings indicate that LLMs effectively capture static affective signals from text, whereas short-term affective variation in this dataset is more strongly explained by recent numeric state trajectories than by textual semantics."
}