Paper Info
Title
Understanding Iterative Revision from Human-Written Text
Abstract
Writing is, by nature, a strategic, adaptive, and more importantly, an iterative process. A crucial part of writing is editing and revising the text. Previous works on text revision have focused on defining edit intention taxonomies within a single domain or developing computational models with a single level of edit granularity, such as sentence-level edits, which differ from human's revision cycles. This work describes ITERATER: the first large-scale, multi-domain, edit-intention annotated corpus of iteratively revised text. In particular, ITERATER is collected based on a new framework to comprehensively model the iterative text revisions that generalize to various domains of formal writing, edit intentions, revision depths, and granularities. When we incorporate our annotated edit intentions, both generative and edit-based text revision models significantly improve automatic evaluations.(1) Through our work, we better understand the text revision process, making vital connections between edit intentions and writing quality, enabling the creation of diverse corpora to support computational modeling of iterative text revisions.
Year
DOI
Venue
2022
10.18653/v1/2022.acl-long.250
PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS)
DocType
Volume
Citations 
Conference
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Wanyu Du100.34
Vipul Raheja200.34
Dhruv Kumar300.34
Zae Myung Kim400.34
Melissa Lopez500.34
Dongyeop Kang600.34