CUNI at SemEval-2026 Task 4: Multi-Head Narrative Aspect Disentanglement via Entangled Synthetic Dataset
Year: 2026
Type: conference-paper
Abstract: We participate in Track B of the SemEval 2026 Task 4 on narrative similarity, focusing on narrative representation learning.We introduce a synthetic dataset designed to disentangle core narrative aspects-abstract theme, course of action, and outcome-and propose a multi-head multi-positive extension of the InfoNCE objective to train aspect-specific embeddings.Our best model achieves 64.25% accuracy on the test set.A nearest-centroid analysis indicates partial aspect-specific structure in the submitted checkpoint, while the training dynamics reveal a partial misalignment between the contrastive objective and the triplet-based evaluation protocol.
Authors: Jan Mitka, Jindrich Helcl
Institutions: Charles University, University of Oslo
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166860562