@inproceedings{song-etal-2026-ynu,
title = "{YNU}-{HPCC} at {S}em{E}val-2026 Task 4: Narrative Similarity via Multi-Perspective E5-Mistral and Embedding Routing",
author = "Song, Feiyang and
Wang, Jin and
Zhang, Xuejie",
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-1.63/",
pages = "440--445",
ISBN = "979-8-89176-414-9",
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 instruction-tuned 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 structure-preserving 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."
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<title>YNU-HPCC at SemEval-2026 Task 4: Narrative Similarity via Multi-Perspective E5-Mistral and Embedding Routing</title>
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<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 instruction-tuned 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 structure-preserving 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.</abstract>
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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2026 Task 4: Narrative Similarity via Multi-Perspective E5-Mistral and Embedding Routing
%A Song, Feiyang
%A Wang, Jin
%A Zhang, Xuejie
%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 song-etal-2026-ynu
%X 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 instruction-tuned 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 structure-preserving 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.
%U https://aclanthology.org/2026.semeval-1.63/
%P 440-445
Markdown (Informal)
[YNU-HPCC at SemEval-2026 Task 4: Narrative Similarity via Multi-Perspective E5-Mistral and Embedding Routing](https://aclanthology.org/2026.semeval-1.63/) (Song et al., SemEval 2026)
ACL