@inproceedings{istrate-etal-2026-narrative,
    title = "Narrative Team at {S}em{E}val-2026 Task 5: Rating Plausibility of Word Senses in Ambiguous Sentences through Narrative Understanding",
    author = "Istrate, Valentin  and
      Octavian, Mocanu  and
      Khaidukova, Tatiana",
    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-1.148/",
    pages = "1089--1093",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our system for SemEval-2026 Task 5, which focuses on predicting the plausibility of word senses in ambiguous narrative contexts. The task requires assigning a real-valued plausibility score to candidate word senses based on aggregated human judgments. Our approach compares two modeling paradigms: (i) a pretrained transformer-based regression model using DistilBERT fine-tuned on the task data, and (ii) a lightweight neural baseline based on a bidirectional LSTM trained either from scratch or initialized with GloVe embeddings. Input representations combine a candidate sense definition with the narrative context and target sentence, separated by a special token. On the official test set, the DistilBERT model achieves the strongest result among our submissions, with an Acc@SD score of 0.54 and Spearman correlation of 0.17, while the best BiLSTM submission reaches 0.52 Acc@SD and 0.02 Spearman correlation. Although DistilBERT performs best in our experiments, the recurrent baseline remains competitive under the tolerance-based metric. We discuss model variants, reproducibility details, and limitations of our analysis."
}