@inproceedings{borjigin-yang-2026-dutir,
    title = "{DUTIR} at {S}em{E}val-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning",
    author = "Borjigin, Tala  and
      Yang, Liang",
    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-1.378/",
    pages = "3010--3014",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents our approach for SemEval 2026 Task 4. Our method leverages a large language model fine-tuned via Low-Rank Adaptation, incorporates data cleaning, and employs a multi-prompt strategy, all trained on the official synthetic dataset. Evaluated on Track A, our system achieved an official score of 0.70, representing a reasonable performance under the given task constraints. In addition, we explore an alternative contrastive learning framework originally designed for Track B, where narrative-structure embeddings are learned and subsequently applied to Track A via similarity comparisons. Our analysis suggests that direct supervised adaptation may be more suitable for narrative reasoning tasks."
}