@inproceedings{rathva-2026-agi,
    title = "``{AGI}'' Team at {S}em{E}val-2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays",
    author = "Rathva, Harsh",
    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-1.21/",
    pages = "140--145",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our submission to SemEval-2026 Task 2: Predicting Variation in Emotional Valence and Arousal. We combine RoBERTa-Large text encoding with a unidirectional GRU for temporal modeling and gated user embeddings for personalization. A four-phase staged training curriculum employs ordinal regression for absolute affect prediction and a zero-inflated delta model for change detection. Our approach achieves competitive performance on Subtask 1 (longitudinal affect assessment) with composite correlation r=0.600 for valence and r=0.452 for arousal. However, we observe systematic degradation in Subtask 2A (state change detection) with negative correlations (r=-0.167 for valence, r=-0.147 for arousal), revealing a fundamental trade-off between stability-oriented representations and change sensitivity. We provide detailed empirical analysis of these failure modes, contributing insights into the challenges of modeling emotional dynamics in ecological data.Code and trained checkpoints are publicly available."
}