@inproceedings{beppu-etal-2026-curiosai,
    title = "{C}urios{AI} at {S}em{E}val-2026 Task 2: Predicting Emotion using {R}o{BERT}a-large model",
    author = "Beppu, Fumika  and
      Takushima, Hiroki  and
      Manoj, Aiswariya  and
      Yamaga, Daichi  and
      Shibata, Yuki  and
      Hori, Takayuki",
    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-1.18/",
    pages = "121--126",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper proposes a method for predicting continuous emotion dimensions, namely Valence and Arousal, from text by combining affective intermediate training with multi-task learning. The proposed approach consists of two training phases: an intermediate pre-training phase using external emotion datasets, followed by a multi-task learning phase using task-specific data. RoBERTa-large is employed as the backbone model, and independent regression heads are introduced for each subtask. Experimental results show that the proposed method achieves Pearson correlation coefficients of 0.68 for Valence and 0.45 for Arousal on Subtask 1, demonstrating stable performance, particularly in capturing inter-user differences in emotional expression."
}