PEU Lab at SemEval-2026 Task 4: Pairwise Text Comparison using RoBERTa and Ranking Loss

AbstractThis 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.