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: https://github.com/dinhthienan33/SemEval2026-Task4-ttda704.

翻译：我们介绍参与SemEval 2026任务四——叙事故事相似性与叙事表示学习的方法。本方案采用对比学习结合微调后的句子Transformer，以捕捉跨抽象主题、行动过程与结果层面的叙事相似性。我们开发了两条流水线：（赛道A）单视角方法，通过智能层冻结对完整叙事进行编码以减少过拟合；（赛道B）多视角方法，采用视角专用投影头与自监督对齐，分别对主题、情节和结局进行建模。两条流水线均基于句子Transformer模型，并在合成数据上使用对比损失进行训练。相关代码已发布于以下GitHub仓库：https://github.com/dinhthienan33/SemEval2026-Task4-ttda704。