This paper presents results of our system for CoMeDi Shared Task, focusing on Subtask 2: Disagreement Ranking. Our system leverages sentence embeddings generated by the paraphrase-xlm-r-multilingual-v1 model, combined with a deep neural regression model incorporating batch normalization and dropout for improved generalization. By predicting the mean of pairwise judgment differences between annotators, our method explicitly targets disagreement ranking, diverging from traditional "gold label" aggregation approaches. We optimized our system with a customized architecture and training procedure, achieving competitive performance in Spearman correlation against mean disagreement labels. Our results highlight the importance of robust embeddings, effective model architecture, and careful handling of judgment differences for ranking disagreement in multilingual contexts. These findings provide insights into the use of contextualized representations for ordinal judgment tasks and open avenues for further refinement of disagreement prediction models.

本研究针对CoMeDi共享任务中的子任务2——分歧排名，提出了一种新方法，使用基于paraphrase-xlm-r-multilingual-v1模型生成的句子嵌入和深度神经回归模型。通过优化模型架构与训练程序，我们的系统在Spearman相关性表现上达到了竞争性水平，这表明在多语言环境中有效处理判断差异和使用强大的句子嵌入对于分歧排名的重要性。

FuocChuVIP123在CoMeDi共享任务中的表现：利用XLM-Roberta句子嵌入和深度神经回归进行分歧排名