Year: 2026
Type: conference-paper
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.
Institution: Puer University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166870283