---
title: "YNU-HPCC at SemEval-2026 Task 4: Narrative Similarity via"
description: "The authors tackle SemEval-2026 Task 4, which asks whether two stories share the… 0.7425 accuracy. 17 references, cited by 1 paper here."
authors: ["Feiyang Song", "Jin Wang", "Xuejie Zhang"]
year: 2026
venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)"
source: "https://aclanthology.org/2026.semeval-1.63/"
doi: "10.18653/v1/2026.semeval-1.63"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/song-2026-ynu-hpcc-semeval-2026-task/"
---
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# YNU-HPCC at SemEval-2026 Task 4: Narrative Similarity via Multi-Perspective E5-Mistral and Embedding Routing

**Authors:** Feiyang Song, [Jin Wang](https://md.research.elamur.ai/free/en/authors/jin-wang/) and [Xuejie Zhang](https://md.research.elamur.ai/free/en/authors/xuejie-zhang/) · **Year:** 2026 · **Venue:** [Proceedings of the 20th International Workshop on Semantic Evaluation (2026)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2026](https://md.research.elamur.ai/free/en/venues/semeval/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.semeval-1.63/) · [PDF](https://aclanthology.org/2026.semeval-1.63.pdf) · [DOI 10.18653/v1/2026.semeval-1.63](https://doi.org/10.18653/v1/2026.semeval-1.63) · [OpenAlex](https://openalex.org/W7166831225)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The authors tackle SemEval-2026 Task 4, which asks whether two stories share the same underlying narrative (abstract theme, course of action, outcomes) regardless of wording, characters or setting. Standard dense encoders tend to reward lexical overlap, so they struggle with pairs that look alike on the surface but resolve differently.

For Track A (triplet ranking), the team fine-tuned E5-Mistral-7B-Instruct with LoRA and MNRL on 10,000 synthetic triplets generated by Gemini-2.5-Pro. Each story is encoded as three fused views: theme, verb-based action, and ending-based outcome. When the cosine-similarity gap between the two candidates is small, the triplet is passed to Gemini-3-Flash, which reasons with chain-of-thought and majority voting.

For Track B (representation learning), they used Gemini-Embedding-001 on head-body-tail excerpts and then applied ABTT post-processing.

The system placed 5th in Track A with 0.7425 accuracy and 2nd in Track B with 0.7125. The results suggest that routing uncertain cases from a cheap encoder to an LLM, combined with large general-purpose embedders, works well for narrative-level similarity.

## Key points
- Track A routing: the view embeddings are weighted 0.5 (theme), 0.25 (action) and 0.25 (outcome). Triplets with a margin below τ = 0.072, a value tuned by grid search on the dev set, go to Gemini-3-Flash, sampled three times at temperature 1.0 with majority voting.
- Dev-set scores:
  - E5-Mistral rose from 0.670 zero-shot to 0.705 after fine-tuning.
  - On the hard subset, Gemini-3-Flash (0.730) beat GPT-5.2 (0.695) and Qwen3-Max (0.675).
  - The full hybrid reached 0.750 on the dev set.
- Data augmentation: ROCStories and the CMU Book Summary dataset were dropped because of domain shift. The final training data was LLM-synthesized hard negatives that follow the official annotation guidelines.
- Track B setup: Gemini-Embedding-001 (768 dimensions, task type SEMANTIC_SIMILARITY) encodes the first 3, 3 sampled middle, and last 3 sentences of each story. ABTT then removes the first principal component from the embeddings. This setup outperformed the fine-tuned E5-Mistral, which scored 0.6600 on the test set.
- Official baselines for comparison:
  - Track A: GPT-4o-mini scored 0.6700.
  - Track B: story-emb scored 0.6325.
  - Future work: distilling Gemini-3-Flash's reasoning into small local bi-encoders.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Yunnan University](https://md.research.elamur.ai/free/en/universities/yunnan-university/) — University, Kunming, China · 17 works · 40 citations

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## Cite

Cite as: Feiyang Song, Jin Wang and Xuejie Zhang. 2026. YNU-HPCC at SemEval-2026 Task 4: Narrative Similarity via Multi-Perspective E5-Mistral and Embedding Routing. Proceedings of the 20th International Workshop on Semantic Evaluation (2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{song2026ynu,
  title = {{YNU-HPCC at SemEval-2026 Task 4: Narrative Similarity via Multi-Perspective E5-Mistral and Embedding Routing}},
  author = {Feiyang Song, Jin Wang and Xuejie Zhang},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.63},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.63}
}
```

- [← previous YNU-HPCC at SemEval-2026 Task 1: Constraint-Aware In-Context Learning for Multilingual Humor Generation](https://md.research.elamur.ai/free/en/papers/zhang-2026-ynu-hpcc-semeval-2026-task/)
- [next → YNU-NLP at SemEval-2026 Task 11: A Neuro-Symbolic Approach with Reflexion Mechanism Disentangling Content and Formal Reasoning in Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-ynu-nlp-semeval-2026-task/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/19/)

## References

17 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 31 October 2026*