@inproceedings{zhou-etal-2026-team,
    title = "Team {HITS} at {S}em{E}val-2026 Task 4:Enhancing narrative text embedding model training with hard negatives generation and self-distillation",
    author = "Zhou, Qian  and
      Fan, Yi  and
      Liu, Wei  and
      Strube, Michael",
    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-1.338/",
    pages = "2679--2688",
    ISBN = "979-8-89176-414-9",
    abstract = "We first use Qwen2.5-32B-Instruct model to generate hard negatives from threenarrative dimensions. We then train a Qwen3-Embedding-8B model with a multi-negativecontrastive objective and use self-distllation."
}