@inproceedings{shah-etal-2026-ai4pc,
    title = "{AI}4{PC}-{H}oward {U}niversity at {S}em{E}val-2026 Task 2: Fine-Tuning {D}istil{BERT}, {D}e{BERT}a and {M}odern{BERT} for Valence{--}Arousal Prediction and Change Estimation",
    author = "Shah, Araj  and
      Shah, Utsav  and
      Aryal, Saurav",
    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-1.89/",
    pages = "617--623",
    ISBN = "979-8-89176-414-9",
    abstract = "We present lightweight, reproducible models for longitudinal valence{--}arousal (VA) prediction in the SemEval-2026 Task 2 essay corpus. Using only the official data, we enforce user-disjoint splits to prevent leakage and evaluate three settings: essay-level VA state estimation, short-horizon VA change forecasting, and long-horizon disposition change prediction. Our submitted systems use DistilBERT for essay-level regression, ModernBERT-based history modeling with a GRU and a blended previous-delta baseline for short-horizon change, and pooled DeBERTa history embeddings with a compact MLP for disposition change. On the official evaluation, across our best performing approaches, we achieve rcomp =0.665/0.468 (valence/arousal) for Subtask 1, r = 0.597/0.413 for Subtask 2A, and r =0.046/0.348 for Subtask 2B."
}