@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T NCL HKU-NarrSim at SemEval-2026 Task 4: Aspect-Based Agents and Supervised Contrastive Embeddings for Narrative Similarity
%A Xu, Jianfei
%A Zhu, Ting
%A Chen, Mingyang
%A Liang, Huizhi(elly)
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F xu-etal-2026-ncl
%X 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.
%U https://aclanthology.org/2026.semeval-1.188/
%P 1451-1461
Markdown (Informal)
[NCL HKU-NarrSim at SemEval-2026 Task 4: Aspect-Based Agents and Supervised Contrastive Embeddings for Narrative Similarity](https://aclanthology.org/2026.semeval-1.188/) (Xu et al., SemEval 2026)
ACL