@inproceedings{ruan-etal-2026-pai,
    title = "{PAI} at {S}em{E}val-2026 Task 3: An {LLM} and Data Redistribution Adaptation-Based Predictive Strategy for Valence-Arousal Scores",
    author = "Ruan, Zhihao  and
      Yang, Kaifeng  and
      Chen, Cheng  and
      Dai, Wenwen  and
      Mao, Wenjia",
    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-1.193/",
    pages = "1489--1494",
    ISBN = "979-8-89176-414-9",
    abstract = "To address the valence and arousal score prediction task in Dimensional Aspect-Based Sentiment Analysis (DimABSA), we propose a two-stage strategy. In the first stage, we conduct post-training on a Large Language Model (LLM) via a Supervised Fine-Tuning (SFT) scheme, followed by generating initial predictions for valence and arousal scores. In the second stage, we perform distribution adaptation on the initial results by leveraging the training set distribution through various techniques, including Gaussian distribution modeling, quantile mapping, and the Sinkhorn algorithm."
}