@inproceedings{jones-etal-2026-unf,
    title = "{UNF}-{BMI} at {S}em{E}val-2026 Task 3: Research Domain Criteria-Guided Large Language Models for Dimensional Aspect-Based Sentiment Analysis",
    author = "Jones, Athlene  and
      Shah, Vishwaa  and
      Kahanda, Indika",
    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-1.321/",
    pages = "2540--2547",
    ISBN = "979-8-89176-414-9",
    abstract = "We present UNF-BMI system for SemEval-2026 Task 3, Track A, Subtask 1 (Dimensional Aspect Sentiment Regression, DimASR), which focuses on predicting continuous Valence{--}Arousal (VA) scores for aspects in text. Our approach integrates psychologically grounded affective signals inspired by the Research Domain Criteria (RDoC) framework. We investigate two complementary methods: first, an in-context learning framework using Mistral-7B-Instruct with semantically retrieved few-shot examples augmented by lexicon-derived RDoC valence and arousal cues; second, a supervised multi-task learning model based on RoBERTa, where VA regression is the primary objective and RDoC-based positive/negative signal prediction serves as an auxiliary task to regularize shared representations. Experiments on english laptop and restaurant review datasets demonstrate that incorporating RDoC-inspired affective priors reduces RMSE compared to baselines, particularly in low-signal text where explicit sentiment cues are sparse."
}