Team UBSE at SemEval-2026 Task 4: Adapting Generalist Embeddings for Narrative Representations
Year: 2026
Type: conference-paper
Abstract: The Narrative Story Similarity and Narrative Representation Learning (NSNRL) task measures the narrative similarity between two stories based on three core aspects: the abstract theme, the course of action, and the outcomes.Our system leverages LLMs both for extracting high-level aspects and to encode them with state-of-the-art generalist embedding models.We then apply a series of embedding postprocessing steps and learn to fit the embedding space with a Mahalanobis-like diagonal metric.We show that some of these techniques should not be applied universally, as they do not necessarily increase performance or overfit, depending on the base encoder.Our system outperforms the baseline only in Track B, ranking twelfth out of twenty-seven on the final leaderboard, while performing lower than the baseline accuracy in Track A.
Authors: Marius Marogel, Marius Popescu
Institution: University of Bucharest
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Health Resources in Action
Award: G1008782263
OpenAlex ID: W7166839163