通过多视角对齐与伪匿名化，提升故事结构相似性建模效果。
ttda704 at SemEval-2026 Task 4: Modeling Narrative Structures via Pseudonymization and Multi-View Sentence Alignment
- 用对比学习和微调句向量模型，捕捉主题、情节、结果三类叙事特征。
- 双路径设计：单视图减少过拟合，多视图实现跨视角自监督对齐。
- 适用于故事相似性分析与叙事表征学习任务，代码开源可复现。
我们提出针对 SemEval 2026 Task 4 的叙事相似性与叙事表征学习方法。采用对比学习与微调的句向量模型，捕捉抽象主题、情节走向与结果层面的叙事相似性。设计两条流水线：（Track A）单视图方法，通过智能层冻结减少过拟合；（Track B）多视图方法，分别建模主题、情节与结果，并使用视图专属投影头与自监督对齐。两条路径均基于 sentence-transformers 模型，在合成数据上以对比损失训练。代码已公开于 https://github.com/dinhthienan33/SemEval2026-Task4-ttda704。
原文摘要 · Abstract (English)
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.
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