Papers with feedback comment generation
Template-guided Grammatical Error Feedback Comment Generation (2023.eacl-srw)
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| Challenge: | Writing corrective feedback on learner text is widespread in language education, but it can be time-consuming for teachers. |
| Approach: | They propose to use feedback comment generation to generate explanatory notes for learners by categorizing comments and constraining outputs of noisy classes. |
| Outcome: | The proposed scheme can be used to generate feedback comment corpora using a broader scope than existing typologies focused on error correction. |
Creating Corpora for Research in Feedback Comment Generation (2020.lrec-1)
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| Challenge: | Existing corpus of learner corpora with feedback comments is limited due to the lack of public access to this task. |
| Approach: | They describe two corpora that have been manually annotated with feedback comments . they describe how the principle and guidelines for feedback comment annotation work . |
| Outcome: | The proposed corpus is available on the web and will facilitate research in feedback comment generation. |
Toward a Task of Feedback Comment Generation for Writing Learning (D19-1)
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| Challenge: | Existing work on feedback comment generation has been limited . despite its usefulness, there is no publicly available dataset for research on feedback comments . |
| Approach: | They introduce a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning for non-native learners of English. |
| Outcome: | The proposed task is based on a corpus of 1,900 essays with all preposition errors annotated with feedback comments. |
LLM Agents for Education: Advances and Applications (2025.findings-emnlp)
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Zhendong Chu, Shen Wang, Jian Xie, Tinghui Zhu, Yibo Yan, Jingheng Ye, Aoxiao Zhong, Xuming Hu, Jing Liang, Philip S. Yu, Qingsong Wen
| Challenge: | Large Language Model (LLM) agents are transforming education by automating complex tasks and enhancing both teaching and learning processes. |
| Approach: | This survey analyzes recent advances in applying Large Language Model agents to educational settings . it highlights ethical issues, hallucination and overreliance, and integration with existing ecosystems . |
| Outcome: | The authors analyze the technologies enabling LLM agents and highlight key challenges in deploying them in educational settings. |
Exploring Methods for Generating Feedback Comments for Writing Learning (2021.emnlp-main)
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| Challenge: | Existing methods for generating explanatory notes for language learners are inadequate . nagata et al. demonstrates that neural-retrieval-based methods can generate feedback comments for preposition use . |
| Approach: | They investigate three different methods for generating feedback comments for preposition use . grammatical and writing items can also be used to generate feedback comments . |
| Outcome: | The proposed methods outperform neural-retrieval-based methods in generating feedback comments for preposition use. |