@inproceedings{zhou-etal-2026-teleai,
    title = "{T}ele{AI} at {S}em{E}val-2026 Task 3: Large Language Models for Dimensional Aspect-Based Sentiment Analysis",
    author = "Zhou, Yan  and
      Wang, Wangshicheng  and
      Wang, Shiquan  and
      Bao, Mengjiao  and
      Fang, Ruiyu  and
      Song, Shuangyong  and
      Li, Yongxiang  and
      Li, Xuelong",
    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-1.233/",
    pages = "1846--1852",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes TeleAI{'}s system for SemEval-2026 Task 3, Track A, Subtask 1 (DimASR), which focuses on predicting continuous Valence-Arousal (VA) scores for specific aspects in text. We frame this task as an end-to-end regression problem and propose a robust framework utilizing Qwen2.5-7B as the feature extraction backbone, combined with parameter-efficient fine-tuning via LoRA. To enhance model generalization and mitigate domain shifts, we primarily leverage multilingual and multi-domain mixed training. Furthermore, our system integrates several optimization and robustness techniques to stabilize continuous score prediction, including R-Drop-style consistency regularization, embedding-level PGD adversarial training, Smooth L1 (Huber) loss, sigmoid-based output interval mapping, and post-hoc linear calibration. Our comprehensive ablations demonstrate that the combination of joint training and robustness regularizations substantially reduces the official evaluation metric, {\$}RMSE{\{}VA{\}}{\$}. The proposed system achieves highly competitive performance across multiple language and domain settings, demonstrating the efficacy of applying lightweight LLM adaptation for dimensional aspect-based sentiment analysis."
}