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        <title>System Report for CCL25-Eval Task 6: Enhancing Chinese Essay Rhetoric Recognition through Targeted Data Augmentation and Model Ensemble Voting</title>
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    <abstract>“This paper presents our approach to the Second Chinese Essay Rhetoric Identification and Understanding Competition, which focuses on analyzing rhetorical features in essays written by primary and secondary school students. The competition includes three tasks: multi-label classification of rhetorical forms, divided into 9 coarse-grained and 19 fine-grained categories; multi-label classification of rhetorical content, comprising 5 coarse-grained and 11 fine-grained categories specific to certain rhetorical types; and extraction of rhetorical components, including connectives, descriptive objects, and specific rhetorical content. To address the challenge of limited training data, we applied targeted data augmentation and manual corrections to build a high-quality dataset. We then fine-tuned large language models using one-shot and in-context learning. Finally, we employed an ensemble strategy that integrates model predictions through a voting mechanism. Our system achieved a score of 52.78 and ranked third in the competition.”</abstract>
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%0 Conference Proceedings
%T System Report for CCL25-Eval Task 6: Enhancing Chinese Essay Rhetoric Recognition through Targeted Data Augmentation and Model Ensemble Voting
%A Tang, Jingjun
%A Tang, Zhiwen
%Y Lin, Hongfei
%Y Li, Bin
%Y Tan, Hongye
%S Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
%D 2025
%8 August
%I Chinese Information Processing Society of China
%C Jinan, China
%F tang-tang-2025-system-report
%X “This paper presents our approach to the Second Chinese Essay Rhetoric Identification and Understanding Competition, which focuses on analyzing rhetorical features in essays written by primary and secondary school students. The competition includes three tasks: multi-label classification of rhetorical forms, divided into 9 coarse-grained and 19 fine-grained categories; multi-label classification of rhetorical content, comprising 5 coarse-grained and 11 fine-grained categories specific to certain rhetorical types; and extraction of rhetorical components, including connectives, descriptive objects, and specific rhetorical content. To address the challenge of limited training data, we applied targeted data augmentation and manual corrections to build a high-quality dataset. We then fine-tuned large language models using one-shot and in-context learning. Finally, we employed an ensemble strategy that integrates model predictions through a voting mechanism. Our system achieved a score of 52.78 and ranked third in the competition.”
%U https://aclanthology.org/2025.ccl-2.29/
%P 244-251

Markdown (Informal)

[System Report for CCL25-Eval Task 6: Enhancing Chinese Essay Rhetoric Recognition through Targeted Data Augmentation and Model Ensemble Voting](https://aclanthology.org/2025.ccl-2.29/) (Tang & Tang, CCL 2025)

• System Report for CCL25-Eval Task 6: Enhancing Chinese Essay Rhetoric Recognition through Targeted Data Augmentation and Model Ensemble Voting (Tang & Tang, CCL 2025)

ACL

• Jingjun Tang and Zhiwen Tang. 2025. System Report for CCL25-Eval Task 6: Enhancing Chinese Essay Rhetoric Recognition through Targeted Data Augmentation and Model Ensemble Voting. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 244–251, Jinan, China. Chinese Information Processing Society of China.