@inproceedings{ma-etal-2026-peu,
title = "{PEU} Lab at {S}em{E}val-2026 Task 4: Pairwise Text Comparison using {R}o{BERT}a and Ranking Loss",
author = "Ma, Hangchao and
Dao, Jiaxu and
Tong, Jinli and
Li, Zhuoying and
Zhou, Qingsong and
Tang, Xiuzhong",
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-1.136/",
pages = "988--993",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes the system developed by the PEU Lab for SemEval-2026 Task 4, specifically focusing on Track A: Comparative Narrative Similarity. To address the pairwise nature of the task, a lightweight contrastive ranking approach is proposed. Specifically, the pretrained RoBERTa-Large model is utilized to encode the anchor and candidate stories. Rather than employing standard cross-entropy, a margin ranking loss is introduced, which allows the relative narrative proximity between different candidate stories to be explicitly modeled. Furthermore, a 5-fold cross-validation ensemble strategy is integrated to stabilize predictions on unseen data. Evaluated on the official dataset, the optimal configuration achieved an overall accuracy of 64.50{\%}, demonstrating the effectiveness of relative order modeling. The code for this system is available at: https://github.com/mhchhh/SemEval2026-Task-4."
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<abstract>This paper describes the system developed by the PEU Lab for SemEval-2026 Task 4, specifically focusing on Track A: Comparative Narrative Similarity. To address the pairwise nature of the task, a lightweight contrastive ranking approach is proposed. Specifically, the pretrained RoBERTa-Large model is utilized to encode the anchor and candidate stories. Rather than employing standard cross-entropy, a margin ranking loss is introduced, which allows the relative narrative proximity between different candidate stories to be explicitly modeled. Furthermore, a 5-fold cross-validation ensemble strategy is integrated to stabilize predictions on unseen data. Evaluated on the official dataset, the optimal configuration achieved an overall accuracy of 64.50%, demonstrating the effectiveness of relative order modeling. The code for this system is available at: https://github.com/mhchhh/SemEval2026-Task-4.</abstract>
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%0 Conference Proceedings
%T PEU Lab at SemEval-2026 Task 4: Pairwise Text Comparison using RoBERTa and Ranking Loss
%A Ma, Hangchao
%A Dao, Jiaxu
%A Tong, Jinli
%A Li, Zhuoying
%A Zhou, Qingsong
%A Tang, Xiuzhong
%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 ma-etal-2026-peu
%X This paper describes the system developed by the PEU Lab for SemEval-2026 Task 4, specifically focusing on Track A: Comparative Narrative Similarity. To address the pairwise nature of the task, a lightweight contrastive ranking approach is proposed. Specifically, the pretrained RoBERTa-Large model is utilized to encode the anchor and candidate stories. Rather than employing standard cross-entropy, a margin ranking loss is introduced, which allows the relative narrative proximity between different candidate stories to be explicitly modeled. Furthermore, a 5-fold cross-validation ensemble strategy is integrated to stabilize predictions on unseen data. Evaluated on the official dataset, the optimal configuration achieved an overall accuracy of 64.50%, demonstrating the effectiveness of relative order modeling. The code for this system is available at: https://github.com/mhchhh/SemEval2026-Task-4.
%U https://aclanthology.org/2026.semeval-1.136/
%P 988-993
Markdown (Informal)
[PEU Lab at SemEval-2026 Task 4: Pairwise Text Comparison using RoBERTa and Ranking Loss](https://aclanthology.org/2026.semeval-1.136/) (Ma et al., SemEval 2026)
ACL