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        <title>Multi-Perspective Document Revision</title>
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    <abstract>This paper presents a novel multi-perspective document revision task. In conventional studies on document revision, tasks such as grammatical error correction, sentence reordering, and discourse relation classification have been performed individually; however, these tasks simultaneously should be revised to improve the readability and clarity of a whole document. Thus, our study defines multi-perspective document revision as a task that simultaneously revises multiple perspectives. To model the task, we design a novel Japanese multi-perspective document revision dataset that simultaneously handles seven perspectives to improve the readability and clarity of a document. Although a large amount of data that simultaneously handles multiple perspectives is needed to model multi-perspective document revision elaborately, it is difficult to prepare such a large amount of this data. Therefore, our study offers a multi-perspective document revision modeling method that can use a limited amount of matched data (i.e., data for the multi-perspective document revision task) and external partially-matched data (e.g., data for the grammatical error correction task). Experiments using our created dataset demonstrate the effectiveness of using multiple partially-matched datasets to model the multi-perspective document revision task.</abstract>
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%0 Conference Proceedings
%T Multi-Perspective Document Revision
%A Ihori, Mana
%A Sato, Hiroshi
%A Tanaka, Tomohiro
%A Masumura, Ryo
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F ihori-etal-2022-multi
%X This paper presents a novel multi-perspective document revision task. In conventional studies on document revision, tasks such as grammatical error correction, sentence reordering, and discourse relation classification have been performed individually; however, these tasks simultaneously should be revised to improve the readability and clarity of a whole document. Thus, our study defines multi-perspective document revision as a task that simultaneously revises multiple perspectives. To model the task, we design a novel Japanese multi-perspective document revision dataset that simultaneously handles seven perspectives to improve the readability and clarity of a document. Although a large amount of data that simultaneously handles multiple perspectives is needed to model multi-perspective document revision elaborately, it is difficult to prepare such a large amount of this data. Therefore, our study offers a multi-perspective document revision modeling method that can use a limited amount of matched data (i.e., data for the multi-perspective document revision task) and external partially-matched data (e.g., data for the grammatical error correction task). Experiments using our created dataset demonstrate the effectiveness of using multiple partially-matched datasets to model the multi-perspective document revision task.
%U https://aclanthology.org/2022.coling-1.535
%P 6128-6138

Markdown (Informal)

[Multi-Perspective Document Revision](https://aclanthology.org/2022.coling-1.535) (Ihori et al., COLING 2022)

• Multi-Perspective Document Revision (Ihori et al., COLING 2022)

ACL

• Mana Ihori, Hiroshi Sato, Tomohiro Tanaka, and Ryo Masumura. 2022. Multi-Perspective Document Revision. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6128–6138, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.