@inproceedings{le-etal-2026-vap,
    title = "{VAP}-{G}ame{C}ontroller at {S}em{E}val-2026 Task 2: Lexical-based and Emotion-Aware Approaches for Longtitudinal Emotion Prediction",
    author = "Le, Huy  and
      Phu, Truong  and
      Tran, Trung  and
      Nguyen, Nga  and
      Choudhury, Monojit",
    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-1.37/",
    pages = "256--262",
    ISBN = "979-8-89176-414-9",
    abstract = "In this work, we participate in SemEval-2026 Task 2, which focuses on predicting continuous valence and arousal trajectories from longitudinal ecological essays. To model fine-grained emotional dynamics, we explore three approaches: (1) hierarchical encoder-based models to capture contextual emotional patterns, (2) a lexicon-based pipeline with linguistic rules and a dual-level calibration mechanismfor personalized estimation, and (3) a hybrid framework that integrates lexical emotional signals into neural encoders. Experiments on the official dataset, evaluated using Pearson correlation (r) and MAE, show consistent improvements over baseline methods, highlighting the complementary strengths of neural representations and calibrated lexical features."
}