Understanding Iterative Revision from Human-Written Text
- URL: http://arxiv.org/abs/2203.03802v1
- Date: Tue, 8 Mar 2022 01:47:42 GMT
- Title: Understanding Iterative Revision from Human-Written Text
- Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez,
Dongyeop Kang
- Abstract summary: IteraTeR is the first large-scale, multi-domain, edit-intention annotated corpus of iteratively revised text.
We better understand the text revision process, making vital connections between edit intentions and writing quality.
- Score: 10.714872525208385
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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.
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