@inproceedings{strothe-etal-2026-teamlasse,
    title = "{T}eam{L}asse at {S}em{E}val-2026 Task 3: A Hybrid Generative-Discriminative Framework for Dimensional Aspect-Based Sentiment Analysis",
    author = "Strothe, Lasse  and
      Kolli, Shaghayegh  and
      Diesner, Jana",
    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-1.273/",
    pages = "2155--2162",
    ISBN = "979-8-89176-414-9",
    abstract = "In this paper, we present our system for SemEval-2026 Task 3 Track A: Dimensional Aspect-Based Sentiment Analysis (DimABSA). The core objective is to extract structural sentiment elements{---}such as aspects, opinions, and categories{---}from text and predict their corresponding continuous Valence-Arousal (VA) scores. The primary challenge lies in simultaneously handling structural extraction and continuous numerical regression across highly imbalanced datasets encompassing multiple languages and domains. To address this complexity, we propose a decoupled, two-stage hybrid generative-discriminative framework. A generative Large Language Model first extracts structured sentiment tuples, while an encoder-based language model performs the continuous VA regression. To foster cross-lingual and cross-domain generalization, we train our models using a targeted data balancing mechanism."
}