@inproceedings{rynowiecki-van-der-goot-2026-team,
    title = "Team {BOBW} (Best Of Both Worlds) at {S}em{E}val-2026 Task 3: Modular Cross-Attention Encoders for Dimensional Aspect-Based Sentiment Analysis",
    author = "Rynowiecki, Michal  and
      Van Der Goot, Rob",
    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-1.179/",
    pages = "1385--1390",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents our system for SemEval-2026 Task 3, which identifies four-part opiniondetails in product reviews. We used a sequenceof pairs of BERT encoder models connectedby cross-attention layers. The cross-attentionmechanism provided marginally better resultsthan a self-attention equivalent, failing to show-case a significant improvement. Error propaga-tion through the pipeline hurt the correctness ofthe outputs, with certain stages collapsing thescores. The pipeline architecture{'}s performancewas largely independent of model size, sug-gesting that small modular encoders for down-stream tasks are an efficient alternative to largedecoder models. Our best model got a cF1score of 0.53 on restaurant data and 0.26 onlaptop data."
}