Multi-Perspective Document Revision

Mana Ihori, Hiroshi Sato, Tomohiro Tanaka, Ryo Masumura


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.
Anthology ID:
2022.coling-1.535
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6128–6138
Language:
URL:
https://aclanthology.org/2022.coling-1.535
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Multi-Perspective Document Revision (Ihori et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.535.pdf
Data
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