@inproceedings{amorim-etal-2026-liaad,
    title = "{LIAAD} {INESCTEC} at {S}em{E}val-2026 Task 4: Unsupervised Narrative Similarity via Discourse Representation Structures and Sentence Embeddings",
    author = "Amorim, Evelin  and
      Jorge, Al{\'i}pio  and
      Silvano, Purifica{\c{c}}{\~a}o",
    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-1.277/",
    pages = "2193--2199",
    ISBN = "979-8-89176-414-9",
    abstract = "In this paper, we describe an unsupervised approach using Discourse Representation Structures (DRS) for the SemEval-2026 Task 4. This task was Narrative Similarity and was formulated in two different tracks. Our team only developed a solution for track A, where the input is composed of a triplet: an anchor story, a story A, and a story B. The output in this formulation is to predict which story, A or B, is more similar to the anchor story. Our approach parsed each story and transformed in a DRS format,then we leveraged its structure and extracted features, performing ablation experiments inthe development dataset. Our strategy achieved 0.5975 accuracy in the official blind test set."
}