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YNU-HPCC at SemEval-2026 Task 4: Narrative Similarity via Multi-Perspective E5-Mistral and Embedding Routing

Year: 2026
Type: conference-paper
Abstract: This paper presents the system developed by the YNU-HPCC team for SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning.The task challenges computational systems to identify narrative similarity across three orthogonal dimensions: abstract theme, course of action, and outcomes.The primary scientific difficulty lies in distinguishing the underlying structural fabula from surface-level lexical overlaps, particularly when facing long-context narratives with subtle plot twists.To address this, our approach employs a hybrid architecture that strategically decouples retrieval and ranking tasks.For Track A, we introduce a dynamic routing mechanism where an instructiontuned E5-Mistral-7B model handles clear cases, while ambiguous hard samples are routed to a Gemini-3-Flash reasoner.For Track B, we leverage the global semantic modeling capabilities of Gemini-Embedding-001 via a structurepreserving chunking strategy, enhanced by All-But-The-Top (ABTT) during inference.Extensive experiments on the official test set show that this divide-and-conquer strategy effectively balances local instruction following with global open-domain generalization.Our system performs competitively, ranking 5th in Track A and 2nd in Track B among all participating teams.
DOI: 10.18653/v1/2026.semeval-1.63
Institution: Yunnan University
Language: English
Open Access status: hybrid
OpenAlex ID: W7166831225
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