@inproceedings{zhuang-etal-2024-toree,
title = "{TOREE}: Evaluating Topic Relevance of Student Essays for {C}hinese Primary and Middle School Education",
author = "Zhuang, Xinlin and
Wu, Hongyi and
Shen, Xinshu and
Yu, Peimin and
Yi, Gaowei and
Chen, Xinhao and
Hu, Tu and
Chen, Yang and
Ren, Yupei and
Zhang, Yadong and
Song, Youqi and
Liu, Binxuan and
Lan, Man",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.342/",
doi = "10.18653/v1/2024.findings-acl.342",
pages = "5749--5765",
abstract = "Topic relevance of an essay demands that the composition adheres to a clear theme and aligns well with the essay prompt requirements, a critical aspect of essay quality evaluation. However, existing research of Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback, while Automatic Essay Comment Generation (AECG) faces much complexity and difficulty. Additionally, current Large Language Models, including GPT-4, often make incorrect judgments and provide overly impractical feedback when evaluating topic relevance. This paper introduces \textbf{TOREE} (\textbf{To}pic \textbf{Re}levance \textbf{E}valuation), a comprehensive dataset developed to assess topic relevance in Chinese primary and middle school students' essays, which is beneficial for AES, AECG and other applications. Moreover, our proposed two-step method utilizes TOREE through a combination of Supervised Fine-tuning and Preference Learning. Experimental results demonstrate that TOREE is of high quality, and our method significantly enhances models' performance on two designed tasks for topic relevance evaluation, improving both automatic and human evaluations across four diverse LLMs."
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<abstract>Topic relevance of an essay demands that the composition adheres to a clear theme and aligns well with the essay prompt requirements, a critical aspect of essay quality evaluation. However, existing research of Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback, while Automatic Essay Comment Generation (AECG) faces much complexity and difficulty. Additionally, current Large Language Models, including GPT-4, often make incorrect judgments and provide overly impractical feedback when evaluating topic relevance. This paper introduces TOREE (Topic Relevance Evaluation), a comprehensive dataset developed to assess topic relevance in Chinese primary and middle school students’ essays, which is beneficial for AES, AECG and other applications. Moreover, our proposed two-step method utilizes TOREE through a combination of Supervised Fine-tuning and Preference Learning. Experimental results demonstrate that TOREE is of high quality, and our method significantly enhances models’ performance on two designed tasks for topic relevance evaluation, improving both automatic and human evaluations across four diverse LLMs.</abstract>
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%0 Conference Proceedings
%T TOREE: Evaluating Topic Relevance of Student Essays for Chinese Primary and Middle School Education
%A Zhuang, Xinlin
%A Wu, Hongyi
%A Shen, Xinshu
%A Yu, Peimin
%A Yi, Gaowei
%A Chen, Xinhao
%A Hu, Tu
%A Chen, Yang
%A Ren, Yupei
%A Zhang, Yadong
%A Song, Youqi
%A Liu, Binxuan
%A Lan, Man
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhuang-etal-2024-toree
%X Topic relevance of an essay demands that the composition adheres to a clear theme and aligns well with the essay prompt requirements, a critical aspect of essay quality evaluation. However, existing research of Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback, while Automatic Essay Comment Generation (AECG) faces much complexity and difficulty. Additionally, current Large Language Models, including GPT-4, often make incorrect judgments and provide overly impractical feedback when evaluating topic relevance. This paper introduces TOREE (Topic Relevance Evaluation), a comprehensive dataset developed to assess topic relevance in Chinese primary and middle school students’ essays, which is beneficial for AES, AECG and other applications. Moreover, our proposed two-step method utilizes TOREE through a combination of Supervised Fine-tuning and Preference Learning. Experimental results demonstrate that TOREE is of high quality, and our method significantly enhances models’ performance on two designed tasks for topic relevance evaluation, improving both automatic and human evaluations across four diverse LLMs.
%R 10.18653/v1/2024.findings-acl.342
%U https://aclanthology.org/2024.findings-acl.342/
%U https://doi.org/10.18653/v1/2024.findings-acl.342
%P 5749-5765
Markdown (Informal)
[TOREE: Evaluating Topic Relevance of Student Essays for Chinese Primary and Middle School Education](https://aclanthology.org/2024.findings-acl.342/) (Zhuang et al., Findings 2024)
ACL
- Xinlin Zhuang, Hongyi Wu, Xinshu Shen, Peimin Yu, Gaowei Yi, Xinhao Chen, Tu Hu, Yang Chen, Yupei Ren, Yadong Zhang, Youqi Song, Binxuan Liu, and Man Lan. 2024. TOREE: Evaluating Topic Relevance of Student Essays for Chinese Primary and Middle School Education. In Findings of the Association for Computational Linguistics: ACL 2024, pages 5749–5765, Bangkok, Thailand. Association for Computational Linguistics.