@inproceedings{yang-etal-2026-looploop,
    title = "looploop at {S}em{E}val-2026 Task 3: A Dimensional Aspect-Based Sentiment System with {D}e{BERT}a Regression and Qwen3 Instruction Fine-Tuning",
    author = "Yang, Liu  and
      Hu, Gang  and
      Li, Jing",
    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-1.196/",
    pages = "1511--1517",
    ISBN = "979-8-89176-414-9",
    abstract = "Aspect-Based Sentiment Analysis (ABSA) hasevolved to capture continuous affective states,posing challenges for traditional classificationmodels. We adopt a hybrid approach tailoredto the varying complexities of the subtasks. ForTask 1 (Valence-Arousal Regression), we em-ploy a discriminative architecture using pre-trained DeBERTa encoder with a MeanPool-ing mechanism to directly regress continuoussentiment scores. For Tasks 2 and 3, which re-quire complex structural extraction of opiniontriplets and quadruplets, we utilize a generativeapproach by fine-tuning the Qwen3-4B-Instructlarge language model via 4-bit QLoRA. Oursystem effectively handles both precise numer-ical regression and complex structural text gen-eration, achieving competitive results acrossthe English laptop and restaurant domains."
}