Understanding Iterative Revision from Human-Written Text
This work addresses the need for better computational tools to support iterative writing processes across various domains, though it is incremental in building on prior edit intention taxonomies.
The authors tackled the problem of modeling iterative text revision by creating IteraTeR, a large-scale, multi-domain corpus annotated with edit intentions, which improved generative and edit-based revision models in automatic evaluations.
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.