ttda704 at SemEval-2026 Task 4: Modeling Narrative Structures via Pseudonymization and Multi-View Sentence Alignment
arXiv:2606.15783v1 Announce Type: new Abstract: 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.
The paper leverages recent advancements in contrastive learning and fine-tuned sentence transformers, aligning with ongoing efforts within the AI research community to enhance narrative understanding and representation.
Improved narrative understanding in AI has broad implications for applications requiring nuanced text comprehension, potentially accelerating the development of more human-like AI agents and complex content analysis.
This research contributes methods for more robust and multi-faceted modeling of narrative structures, potentially leading to AI systems that better capture abstract themes, plot, and outcomes in textual data.
- · AI research community
- · NLP developers
- · Content analysis platforms
Enhanced AI capability to summarize, compare, and generate complex stories and reports accurately.
Development of more sophisticated AI assistants capable of understanding and engaging with user narratives across various domains.
Potential for AI to automate creative writing or investigative journalism by understanding and manipulating narrative structures at a deeper level.
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