@inproceedings{zheng-etal-2025-system,
title = "System Report for {CCL}25-Eval Task 11: Aesthetic Assessment of {C}hinese Handwritings Based on Vision Language Models",
author = "Zheng, Chen and
Lai, Yuxuan and
Lu, Haoyang and
Ma, Wentao and
Yang, Jitao and
Wang, Jian",
editor = "Lin, Hongfei and
Li, Bin and
Tan, Hongye",
booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
month = aug,
year = "2025",
address = "Jinan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2025.ccl-2.53/",
pages = "444--451",
abstract = "``The handwriting of Chinese characters is a fundamental aspect of learning the Chinese language. Previous automated assessment methods often framed scoring as a regression problem. However, this score-only feedback lacks actionable guidance, which limits its effectiveness in helping learners improve their handwriting skills.In this paper, we leverage vision-language models(VLMs) to analyze the quality of handwritten Chinese characters and generate multi-level feedback. Specifically, we investigate two feedback generation tasks: simple grade feedback (Task 1)and enriched, descriptive feedback (Task 2). We explore both low-rank adaptation (LoRA)-based fine-tuning strategies and in-context learning methods to integrate aesthetic assessment knowl-edge into VLMs. Experimental results show that our approach achieves state-of-the-art performances across multiple evaluation tracks in the CCL 2025 workshop on evaluation of handwrittenChinese character quality.''"
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<abstract>“The handwriting of Chinese characters is a fundamental aspect of learning the Chinese language. Previous automated assessment methods often framed scoring as a regression problem. However, this score-only feedback lacks actionable guidance, which limits its effectiveness in helping learners improve their handwriting skills.In this paper, we leverage vision-language models(VLMs) to analyze the quality of handwritten Chinese characters and generate multi-level feedback. Specifically, we investigate two feedback generation tasks: simple grade feedback (Task 1)and enriched, descriptive feedback (Task 2). We explore both low-rank adaptation (LoRA)-based fine-tuning strategies and in-context learning methods to integrate aesthetic assessment knowl-edge into VLMs. Experimental results show that our approach achieves state-of-the-art performances across multiple evaluation tracks in the CCL 2025 workshop on evaluation of handwrittenChinese character quality.”</abstract>
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%0 Conference Proceedings
%T System Report for CCL25-Eval Task 11: Aesthetic Assessment of Chinese Handwritings Based on Vision Language Models
%A Zheng, Chen
%A Lai, Yuxuan
%A Lu, Haoyang
%A Ma, Wentao
%A Yang, Jitao
%A Wang, Jian
%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 zheng-etal-2025-system
%X “The handwriting of Chinese characters is a fundamental aspect of learning the Chinese language. Previous automated assessment methods often framed scoring as a regression problem. However, this score-only feedback lacks actionable guidance, which limits its effectiveness in helping learners improve their handwriting skills.In this paper, we leverage vision-language models(VLMs) to analyze the quality of handwritten Chinese characters and generate multi-level feedback. Specifically, we investigate two feedback generation tasks: simple grade feedback (Task 1)and enriched, descriptive feedback (Task 2). We explore both low-rank adaptation (LoRA)-based fine-tuning strategies and in-context learning methods to integrate aesthetic assessment knowl-edge into VLMs. Experimental results show that our approach achieves state-of-the-art performances across multiple evaluation tracks in the CCL 2025 workshop on evaluation of handwrittenChinese character quality.”
%U https://aclanthology.org/2025.ccl-2.53/
%P 444-451
Markdown (Informal)
[System Report for CCL25-Eval Task 11: Aesthetic Assessment of Chinese Handwritings Based on Vision Language Models](https://aclanthology.org/2025.ccl-2.53/) (Zheng et al., CCL 2025)
ACL