用嵌入+神经回归预测多语言分歧排名，效果优于传统方法。
FuocChuVIP123 at CoMeDi Shared Task: Disagreement Ranking with XLM-Roberta Sentence Embeddings and Deep Neural Regression
- 用XLM-Roberta生成句向量，结合带归一化和丢弃的深度回归模型。
- 在多语言数据上达到与平均分歧标签相当的斯皮尔曼相关性。
- 适合做多语言语义分歧分析的研究者参考。
本文报告了我们在CoMeDi共享任务中针对子任务2：分歧排名的系统结果。系统采用paraphrase-xlm-r-multilingual-v1模型生成的句子嵌入，并结合包含批量归一化和丢弃层的深度神经回归模型以提升泛化能力。通过预测标注者之间成对判断差异的均值，该方法直接面向分歧排名，区别于传统的“黄金标签”聚合方式。我们通过定制化架构与训练流程优化系统，在斯皮尔曼相关性指标上取得了具有竞争力的表现。结果表明，在多语言环境下，鲁棒的嵌入表示、有效的模型结构以及对判断差异的精细处理对分歧排名至关重要。研究为上下文化表示在序数判断任务中的应用提供了新见解，并为分歧预测模型的进一步优化指明方向。
原文摘要 · Abstract (English)
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.
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