Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks

Zae Myung Kim, Wanyu Du, Vipul Raheja, Dhruv Kumar, Dongyeop Kang


Abstract
Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness, or reorganizing sentence structures throughout a document.Most recent research has focused on understanding and classifying different types of edits in the iterative revision process from human-written text instead of building accurate and robust systems for iterative text revision.In this work, we aim to build an end-to-end text revision system that can iteratively generate helpful edits by explicitly detecting editable spans (where-to-edit) with their corresponding edit intents and then instructing a revision model to revise the detected edit spans.Leveraging datasets from other related text editing NLP tasks, combined with the specification of editable spans, leads our system to more accurately model the process of iterative text refinement, as evidenced by empirical results and human evaluations.Our system significantly outperforms previous baselines on our text revision tasks and other standard text revision tasks, including grammatical error correction, text simplification, sentence fusion, and style transfer.Through extensive qualitative and quantitative analysis, we make vital connections between edit intentions and writing quality, and better computational modeling of iterative text revisions.
Anthology ID:
2022.emnlp-main.678
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9986–9999
Language:
URL:
https://aclanthology.org/2022.emnlp-main.678
DOI:
10.18653/v1/2022.emnlp-main.678
Bibkey:
Cite (ACL):
Zae Myung Kim, Wanyu Du, Vipul Raheja, Dhruv Kumar, and Dongyeop Kang. 2022. Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9986–9999, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks (Kim et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.678.pdf