Understanding Iterative Revision from Human-Written Text
By Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang. 2022
78 citations recorded by the source corpus.
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 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. 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.
Subfields: Natural Language Processing
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In Natural Language Processing
- Sebastian Riedel
- Patrick Lewis
- Fabio Petroni
- Timo Schick
- Gautier Izacard
- Edouard Grave
- Hannaneh Hajishirzi
- Zhengbao Jiang
Citations in the corpus
- PEER: A Collaborative Language Model (2022, methodology): “, 2020) is a crowdsourced corpus for single-sentence text simplification; • ITERATER (Du et al., 2022b) is an editing dataset spanning five edit intentions across three different domains;6”
- Learning to Model Editing Processes (2022, unknown): “We also note work (Du et al., 2022a,b) looking at modeling edit intentions and modeling text revision with a more holistic goal in mind.”
- Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models (2023, unknown): “, 2017) and coherence (Du et al., 2022), and evaluate the generated texts from LLMs.”
- Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits (2023, background): “On the other hand, rather than steering the language generation with artificial prompts, humans can easily fix immoral language by making hierarchical and recursive edits (Du et al., 2022; Lee et al., 2022), where human value judgments serve as the guide for each edit.”
- Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision (2022, methodology): “We followed the prior work (Du et al., 2022) to collect the text revision data across three domains: ArXiv, Wikipedia and Wikinews.”