@inproceedings{gruppi-etal-2026-mendel292,
    title = "Mendel292 at {S}em{E}val-2026 Task 4: Disentangled Narrative Embeddings for Story Similarity",
    author = "Gruppi, Mauricio  and
      Rijal, Sankalpa  and
      Debenedetto, Justin",
    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-1.324/",
    pages = "2575--2582",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes Mendel292, our system for SemEval-2026 Task 4 on Narrative Story Similarity. We introduce a narrative encoder that decomposes story representations into explicit subspaces for abstract theme, course of action, and outcome, built on a pre-trained sentence embedding model and trainable BiLSTM projection layer with a triplet margin loss objective. We augment the training set via backtranslation, and incorporate weakly supervised multi-task objectives derived from unsupervised narrative clustering.The proposed architecture was designed to learn a latent representation of narratives in a few-shot setting due to a limited amount of traninig data.Despite using a rich pre-trained transformer, the model was outperformed by a unsupervised pooling approach on the classification task.While our systems do not match the top leaderboard scores, they allow us to systematically study the effects of subspace factorization, weak labels, and data augmentation on narrative similarity modeling."
}