@inproceedings{ho-etal-2026-ualberta,
    title = "{UA}lberta at {S}em{E}val-2026 Task 2: Temporal Fusion Models for Predicting Affect Over Time",
    author = "Ho, Duc  and
      Bui, Khanh  and
      Teodorescu, Daniela  and
      Kondrak, Grzegorz",
    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-1.87/",
    pages = "605--611",
    ISBN = "979-8-89176-414-9",
    abstract = "We describe our systems for the SemEval 2026 Task 2 on Predicting Variation in Emotional Valence and Arousal from Ecological Essays. To predict affect in a single instance, and for forecasting dispositional change, we use embeddings from a language model and a Recurrent Neural Network. To predict state changes from a previous timestep to the next, we use time-series forecasting. Our systems ranked first for forecasting dispositional change, and third for forecasting state change over time. We make our code publicly available."
}