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    <abstract>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 tailored architecture and training procedure, achieving competitive performance in Spearman correlation against the mean disagreement labels. Our results highlights 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 leveraging contextualized representations for ordinal judgment tasks and open avenues for further refinement in disagreement prediction models.</abstract>
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%0 Conference Proceedings
%T FuocChuVIP123 at CoMeDi Shared Task: Disagreement Ranking with XLM-Roberta Sentence Embeddings and Deep Neural Regression
%A Chu, Phuoc Duong Huy
%Y Roth, Michael
%Y Schlechtweg, Dominik
%S Proceedings of Context and Meaning: Navigating Disagreements in NLP Annotation
%D 2025
%8 January
%I International Committee on Computational Linguistics
%C Abu Dhabi, UAE
%F chu-2025-fuocchuvip123
%X 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 tailored architecture and training procedure, achieving competitive performance in Spearman correlation against the mean disagreement labels. Our results highlights 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 leveraging contextualized representations for ordinal judgment tasks and open avenues for further refinement in disagreement prediction models.
%U https://aclanthology.org/2025.comedi-1.9/
%P 97-102

Markdown (Informal)

[FuocChuVIP123 at CoMeDi Shared Task: Disagreement Ranking with XLM-Roberta Sentence Embeddings and Deep Neural Regression](https://aclanthology.org/2025.comedi-1.9/) (Chu, CoMeDi 2025)

• FuocChuVIP123 at CoMeDi Shared Task: Disagreement Ranking with XLM-Roberta Sentence Embeddings and Deep Neural Regression (Chu, CoMeDi 2025)

ACL

• Phuoc Duong Huy Chu. 2025. FuocChuVIP123 at CoMeDi Shared Task: Disagreement Ranking with XLM-Roberta Sentence Embeddings and Deep Neural Regression. In Proceedings of Context and Meaning: Navigating Disagreements in NLP Annotation, pages 97–102, Abu Dhabi, UAE. International Committee on Computational Linguistics.