NCL HKU-NarrSim at SemEval-2026 Task 4: Aspect-Based Agents and Supervised Contrastive Embeddings for Narrative Similarity
Year: 2026
Type: conference-paper
Abstract: SemEval-2026 Task 4 on Narrative Similarity requires models to assess narrative alignment between stories rather than relying on surface lexical similarity.For Track A, we introduce the Aspect-Based Narrative Similarity Agents(ABNS-Agents), a two-stage agent-based framework.It extracts three core narrative aspects aligned with the task definition under a schema constraint, and then performs aspect-aligned similarity adjudication using an LLM decision model.For Track B, Narrative Supervised Contrastive Embeddings(NSConE) is based upon supervised contrastive learning to model narrative similarity.Our experiments show that ABNS-Agents achieves 70.25% accuracy on the test set, while NSConE reaches 68.5% test accuracy, demonstrating competitive performance across both reasoning-based and representation-learning paradigms.The findings highlight the effectiveness of aspect-aligned structured modelling and task-specific supervised contrastive learning for capturing narrative similarity beyond surface semantics.The code is available at https://github.com/jianfeixu95/ NCL-SemEval2026-NarrSim.
Institutions: Newcastle University, University of Hong Kong
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166869418