@inproceedings{phuong-etal-2026-citd,
    title = "{CITD}@{UIT} at {S}em{E}val-2026 Task 2: Temporal Mixture-of-Experts for Longitudinal Valence and Arousal Prediction from Ecological Essays",
    author = "Phuong, Son  and
      Ngo, My  and
      Minh Dao, Tri  and
      Nguyen, Duc-Vu",
    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-1.25/",
    pages = "167--175",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our participation in SemEval-2026 Task 2, which focuses on the longitudinal assessment and forecasting of emotional states through text. The challenge is divided into two primary objectives: Subtask1, which requires estimating continuous Valence and Arousal (V{\&}A) scores for a sequence of texts, and Subtask2, which focuses on forecasting future emotional variations, specifically State Change (2A) and Dispositional Change (2B). To address these tasks, we propose a unified framework based on cardiffnlp/twitter-roberta-base-sentiment-latest, a transformer architecture pretrained on 124 million tweets. For all subtasks, we sort the data chronologically by userid and use a sliding window approach to capture longitudinal context. We conduct extensive experiments combining this pretrained RoBERTa model with Multilayer Perceptron (MLP) and Mixture-of-Experts (MoE) architectures to optimize performance. Furthermore, we utilize both attention pooling and mean pooling on all output hidden state representations to extract richer semantic features. Our proposed system demonstrated competitive performance, officially ranking 9th in Subtask 1 and 5th in Subtask 2A among participating teams."
}