@inproceedings{zhang-etal-2022-automatic-comment,
title = "Automatic Comment Generation for {C}hinese Student Narrative Essays",
author = "Zhang, Zhexin and
Guan, Jian and
Xu, Guowei and
Tian, Yixiang and
Huang, Minlie",
editor = "Che, Wanxiang and
Shutova, Ekaterina",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-demos.21",
doi = "10.18653/v1/2022.emnlp-demos.21",
pages = "214--223",
abstract = "Automatic essay evaluation can help reduce teachers{'} workload and enable students to refine their works rapidly. Previous studies focus mainly on giving discrete scores for either the holistic quality orseveral distinct traits. However, real-world teachers usually provide detailed comments in natural language, which are more informative than single scores. In this paper, we present the comment generation task, which aims to generate commentsfor specified segments from given student narrative essays. To tackle this task, we propose a planning-based generation model, which first plans a sequence of keywords, and then expands these keywords into a complete comment. To improve the correctness and informativeness of generated comments, we adopt two following techniques: (1) training an error correction module to filter out incorrect keywords, and (2) recognizing fine-grained structured features from source essays to enrich the keywords. To support the evaluation of the task, we collect a human-written Chinese dataset, which contains 22,399 essay-comment pairs. Extensive experiments show that our model outperforms strong baselines significantly. Moreover, we exert explicit control on our model to generate comments to describe the strengths or weaknesses of inputs with a 91{\%} success rate. We deploy the model at \url{http://coai.cs.tsinghua.edu.cn/static/essayComment/}. A demo video is available at \url{https://youtu.be/IuFVk8dUxbI}. Our code and data are available at \url{https://github.com/thu-coai/EssayCommentGen}.",
}
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<abstract>Automatic essay evaluation can help reduce teachers’ workload and enable students to refine their works rapidly. Previous studies focus mainly on giving discrete scores for either the holistic quality orseveral distinct traits. However, real-world teachers usually provide detailed comments in natural language, which are more informative than single scores. In this paper, we present the comment generation task, which aims to generate commentsfor specified segments from given student narrative essays. To tackle this task, we propose a planning-based generation model, which first plans a sequence of keywords, and then expands these keywords into a complete comment. To improve the correctness and informativeness of generated comments, we adopt two following techniques: (1) training an error correction module to filter out incorrect keywords, and (2) recognizing fine-grained structured features from source essays to enrich the keywords. To support the evaluation of the task, we collect a human-written Chinese dataset, which contains 22,399 essay-comment pairs. Extensive experiments show that our model outperforms strong baselines significantly. Moreover, we exert explicit control on our model to generate comments to describe the strengths or weaknesses of inputs with a 91% success rate. We deploy the model at http://coai.cs.tsinghua.edu.cn/static/essayComment/. A demo video is available at https://youtu.be/IuFVk8dUxbI. Our code and data are available at https://github.com/thu-coai/EssayCommentGen.</abstract>
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%0 Conference Proceedings
%T Automatic Comment Generation for Chinese Student Narrative Essays
%A Zhang, Zhexin
%A Guan, Jian
%A Xu, Guowei
%A Tian, Yixiang
%A Huang, Minlie
%Y Che, Wanxiang
%Y Shutova, Ekaterina
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F zhang-etal-2022-automatic-comment
%X Automatic essay evaluation can help reduce teachers’ workload and enable students to refine their works rapidly. Previous studies focus mainly on giving discrete scores for either the holistic quality orseveral distinct traits. However, real-world teachers usually provide detailed comments in natural language, which are more informative than single scores. In this paper, we present the comment generation task, which aims to generate commentsfor specified segments from given student narrative essays. To tackle this task, we propose a planning-based generation model, which first plans a sequence of keywords, and then expands these keywords into a complete comment. To improve the correctness and informativeness of generated comments, we adopt two following techniques: (1) training an error correction module to filter out incorrect keywords, and (2) recognizing fine-grained structured features from source essays to enrich the keywords. To support the evaluation of the task, we collect a human-written Chinese dataset, which contains 22,399 essay-comment pairs. Extensive experiments show that our model outperforms strong baselines significantly. Moreover, we exert explicit control on our model to generate comments to describe the strengths or weaknesses of inputs with a 91% success rate. We deploy the model at http://coai.cs.tsinghua.edu.cn/static/essayComment/. A demo video is available at https://youtu.be/IuFVk8dUxbI. Our code and data are available at https://github.com/thu-coai/EssayCommentGen.
%R 10.18653/v1/2022.emnlp-demos.21
%U https://aclanthology.org/2022.emnlp-demos.21
%U https://doi.org/10.18653/v1/2022.emnlp-demos.21
%P 214-223
Markdown (Informal)
[Automatic Comment Generation for Chinese Student Narrative Essays](https://aclanthology.org/2022.emnlp-demos.21) (Zhang et al., EMNLP 2022)
ACL
- Zhexin Zhang, Jian Guan, Guowei Xu, Yixiang Tian, and Minlie Huang. 2022. Automatic Comment Generation for Chinese Student Narrative Essays. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 214–223, Abu Dhabi, UAE. Association for Computational Linguistics.