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        <title>Retrieval, Masking, and Generation: Feedback Comment Generation using Masked Comment Examples</title>
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            <title>Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges</title>
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    <abstract>In this paper, we propose a novel method, retrieval, masking, and generation, for feedback comment generation. Feedback comment generation is a task in which a system generates feedback comments such as hints or explanatory notes for language learners, given input text and position showing where to comment. In the conventional study, the retrieve-and-edit method for retrieving feedback comments in the data pool and editing the comments has been thought effective for this task. However, the performance of this method does not perform as well as other conventional methods because its model learns to edit tokens that do not need to be rewritten in the retrieved comments. To mitigate this problem, we propose a method for combining retrieval, masking, and generation based on the retrieve-and-edit method. Specifically, tokens of feedback comments retrieved from the data pool are masked, and this masked feedback comment is used as a template to generate feedback comments. The proposed method should prevent unnecessary conversion by using not retrieved feedback comments directly but masking them. Our experiments on feedback comment generation demonstrate that the proposed method outperforms conventional methods.</abstract>
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%0 Conference Proceedings
%T Retrieval, Masking, and Generation: Feedback Comment Generation using Masked Comment Examples
%A Ihori, Mana
%A Sato, Hiroshi
%A Tanaka, Tomohiro
%A Masumura, Ryo
%Y Mille, Simon
%S Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F ihori-etal-2023-retrieval
%X In this paper, we propose a novel method, retrieval, masking, and generation, for feedback comment generation. Feedback comment generation is a task in which a system generates feedback comments such as hints or explanatory notes for language learners, given input text and position showing where to comment. In the conventional study, the retrieve-and-edit method for retrieving feedback comments in the data pool and editing the comments has been thought effective for this task. However, the performance of this method does not perform as well as other conventional methods because its model learns to edit tokens that do not need to be rewritten in the retrieved comments. To mitigate this problem, we propose a method for combining retrieval, masking, and generation based on the retrieve-and-edit method. Specifically, tokens of feedback comments retrieved from the data pool are masked, and this masked feedback comment is used as a template to generate feedback comments. The proposed method should prevent unnecessary conversion by using not retrieved feedback comments directly but masking them. Our experiments on feedback comment generation demonstrate that the proposed method outperforms conventional methods.
%U https://aclanthology.org/2023.inlg-genchal.9
%P 60-67

Markdown (Informal)

[Retrieval, Masking, and Generation: Feedback Comment Generation using Masked Comment Examples](https://aclanthology.org/2023.inlg-genchal.9) (Ihori et al., INLG-SIGDIAL 2023)

• Retrieval, Masking, and Generation: Feedback Comment Generation using Masked Comment Examples (Ihori et al., INLG-SIGDIAL 2023)

ACL

• Mana Ihori, Hiroshi Sato, Tomohiro Tanaka, and Ryo Masumura. 2023. Retrieval, Masking, and Generation: Feedback Comment Generation using Masked Comment Examples. In Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges, pages 60–67, Prague, Czechia. Association for Computational Linguistics.