ttda704 at SemEval-2026 Task 4: Modeling Narrative Structures via Pseudonymization and Multi-View Sentence Alignment

Abstract:We present our approach to SemEval 2026 Task 4: Narrative Story Similarity and Narrative Representation Learning. Our solution uses contrastive learning with fine-tuned sentence transformers to capture narrative similarity across abstract themes, course of action, and outcomes. We develop two pipelines: (Track A) a single-view method that encodes full narratives with smart layer freezing to reduce overfitting, and (Track B) a multi-view method that models theme, plot, and outcome with view-specific projection heads and self-supervised alignment. Both pipelines build on sentence-transformers models and are trained with contrastive loss on synthetic data. The code is available at the following GitHub repository: this https URL.

Computation and Language (cs.CL)

arXiv:2606.15783 [cs.CL]

(or arXiv:2606.15783v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.15783 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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