@inproceedings{xu-etal-2026-ncl,
    title = "{NCL} {HKU}-{N}arr{S}im at {S}em{E}val-2026 Task 4: Aspect-Based Agents and Supervised Contrastive Embeddings for Narrative Similarity",
    author = "Xu, Jianfei  and
      Zhu, Ting  and
      Chen, Mingyang  and
      Liang, Huizhi(elly)",
    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-1.188/",
    pages = "1451--1461",
    ISBN = "979-8-89176-414-9",
    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."
}