@inproceedings{benlahbib-etal-2026-nlp-fsdm,
    title = "{NLP}-{FSDM} at {S}em{E}val-2026 Task 4: Narrative Similarity via Multiple Negatives Ranking and Instruction-Based Embeddings",
    author = "Benlahbib, Abdessamad  and
      Essalmani, Zouhir  and
      Boumhidi, Achraf  and
      Fahfouh, Anass  and
      Alami, Hamza",
    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-1.88/",
    pages = "612--616",
    ISBN = "979-8-89176-414-9",
    abstract = "The identification of narrative similarity is a complex NLP challenge that requires modeling deeper plot and thematic alignment rather than relying solely on lexical overlap. In this paper, we detail the participation of team NLP-FSDM in SemEval-2026 Task 4. Our approach utilizes the bge-large-en-v1.5 encoder. For Track A, we fine-tune it using Multiple Negatives Ranking Loss (MNRL), while for Track B we rely on the pretrained encoder to generate fixed narrative representations. We achieved an accuracy of 65.50{\%} in Track A and 62.50{\%} in Track B. This paper provides an extensive comparison of our results with competitive baselines and top-performing systems, analyzing the efficacy of dense encoders in low-resource narrative contexts."
}