@inproceedings{laufer-etal-2026-jct,
    title = "{JCT} 2026 - {S}em{E}val Task 5",
    author = "Laufer, Chava  and
      Turjeman, Batel  and
      Liebeskind, Chaya",
    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-1.115/",
    pages = "826--831",
    ISBN = "979-8-89176-414-9",
    abstract = "The system integrates a generative Large Language Model (Llama-3 8B, fine-tuned via LoRA) with a dual-expert bidirectional cross-encoder (DeBERTa-v3-large) optimized for both semantic similarity and Natural Language Inference (NLI). By aggregating these complementary models, the system effectively captures complex contextual dependencies. In the official test set, our architecture ranked 22nd out of 79 systems, achieving a Spearman Rank Correlation of 0.71 and an accuracy within the standard deviation of 82.04{\%}."
}