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        <title>CophiWue at SemEval-2026 Task 4: Symbolic Narrative Profiling with Taxonomy-Guided Extraction and Contrastive Fine-Tuning</title>
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            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>We present our system for SemEval-2026 Task 4, focusing primarily on Track B (narrative embedding). Our approach, the Decompose &amp; Align Cycle, converts each story into a structured NarrativeProfile consisting of abstract themes, a five-step course of action, and an outcome. We then build a NarrativeTaxonomy from these initial extractions via agglomerative clustering, and use the resulting controlled vocabularies to guide a second extraction pass, producing terminologically standardized profiles across the full dataset. Finally, we contrastively fine-tune the Qwen3-Embedding8B model on profile text representations using TripletLoss, deriving story embeddings from this fine-tuned model. For Track A, we adapt the task’s provided baseline script by substituting Gemini 3 Pro as the judge, using the organizers default prompt on raw story texts.</abstract>
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%0 Conference Proceedings
%T CophiWue at SemEval-2026 Task 4: Symbolic Narrative Profiling with Taxonomy-Guided Extraction and Contrastive Fine-Tuning
%A Konle, Leonard
%A Jannidis, Fotis
%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 konle-jannidis-2026-cophiwue
%X We present our system for SemEval-2026 Task 4, focusing primarily on Track B (narrative embedding). Our approach, the Decompose & Align Cycle, converts each story into a structured NarrativeProfile consisting of abstract themes, a five-step course of action, and an outcome. We then build a NarrativeTaxonomy from these initial extractions via agglomerative clustering, and use the resulting controlled vocabularies to guide a second extraction pass, producing terminologically standardized profiles across the full dataset. Finally, we contrastively fine-tune the Qwen3-Embedding8B model on profile text representations using TripletLoss, deriving story embeddings from this fine-tuned model. For Track A, we adapt the task’s provided baseline script by substituting Gemini 3 Pro as the judge, using the organizers default prompt on raw story texts.
%U https://aclanthology.org/2026.semeval-1.430/
%P 3479-3484

Markdown (Informal)

[CophiWue at SemEval-2026 Task 4: Symbolic Narrative Profiling with Taxonomy-Guided Extraction and Contrastive Fine-Tuning](https://aclanthology.org/2026.semeval-1.430/) (Konle & Jannidis, SemEval 2026)

• CophiWue at SemEval-2026 Task 4: Symbolic Narrative Profiling with Taxonomy-Guided Extraction and Contrastive Fine-Tuning (Konle & Jannidis, SemEval 2026)

ACL

• Leonard Konle and Fotis Jannidis. 2026. CophiWue at SemEval-2026 Task 4: Symbolic Narrative Profiling with Taxonomy-Guided Extraction and Contrastive Fine-Tuning. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3479–3484, San Diego, California, USA. Association for Computational Linguistics.