Understanding Iterative Revision Patterns in Writing
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 revision cycles. This talk will describe 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 generalizes to a variety of domains, edit intentions, revision depths, and granularities. When we incorporate our annotated edit intentions, both generative and action-based text revision models significantly improve automatic evaluations. We show that through our work, we are able to 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.
Vipul Raheja is a Research Scientist at Grammarly. He works on developing robust and scalable Natural Language Processing and Deep Learning approaches for building the next generation of intelligent writing assistance systems, focused on improving the quality of written communication. His research interests lie at the intersection of text editing and controllable text generation. He holds a Masters in Computer Science from Columbia University, where he was affiliated with the Center for Computational and Learning Systems. He received a dual-degree in Computer Science and Engineering from IIIT Hyderabad.