@inproceedings{zhang-yu-2026-cicl26,
title = "{CICL}26 at {S}em{E}val-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning",
author = "Zhang, Wanzhao and
Yu, Yue",
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-1.310/",
pages = "2456--2460",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes our submission to SemEval-2026 Task 4 (Track A) on narrative similarity.The task requires systems to determine which of two candidate stories is more narratively similar to a given anchor story. While large language models (LLMs) demonstrate strong semantic reasoning abilities, their predictions in comparative settings can be sensitive to stochastic decoding and input order.We propose a lightweight inference-time cascade strategy that improves robustness without modifying the underlying model. Our approach combines self-consistency voting to reduce sampling variance,a swap-based symmetry test to mitigate positional bias, and a margin-based deterministic decision rule to resolve disagreements. This design explicitly leverages model uncertainty while maintaining reproducibility and simplicity."
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<abstract>This paper describes our submission to SemEval-2026 Task 4 (Track A) on narrative similarity.The task requires systems to determine which of two candidate stories is more narratively similar to a given anchor story. While large language models (LLMs) demonstrate strong semantic reasoning abilities, their predictions in comparative settings can be sensitive to stochastic decoding and input order.We propose a lightweight inference-time cascade strategy that improves robustness without modifying the underlying model. Our approach combines self-consistency voting to reduce sampling variance,a swap-based symmetry test to mitigate positional bias, and a margin-based deterministic decision rule to resolve disagreements. This design explicitly leverages model uncertainty while maintaining reproducibility and simplicity.</abstract>
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%0 Conference Proceedings
%T CICL26 at SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning
%A Zhang, Wanzhao
%A Yu, Yue
%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 zhang-yu-2026-cicl26
%X This paper describes our submission to SemEval-2026 Task 4 (Track A) on narrative similarity.The task requires systems to determine which of two candidate stories is more narratively similar to a given anchor story. While large language models (LLMs) demonstrate strong semantic reasoning abilities, their predictions in comparative settings can be sensitive to stochastic decoding and input order.We propose a lightweight inference-time cascade strategy that improves robustness without modifying the underlying model. Our approach combines self-consistency voting to reduce sampling variance,a swap-based symmetry test to mitigate positional bias, and a margin-based deterministic decision rule to resolve disagreements. This design explicitly leverages model uncertainty while maintaining reproducibility and simplicity.
%U https://aclanthology.org/2026.semeval-1.310/
%P 2456-2460
Markdown (Informal)
[CICL26 at SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning](https://aclanthology.org/2026.semeval-1.310/) (Zhang & Yu, SemEval 2026)
ACL