Abstract
Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing. Recent work has introduced edit-based models for various tasks (such as neural machine translation and text style transfer), but these generally model a single edit step. In this work, we propose modeling editing processes, modeling the whole process of iteratively generating sequences. We form a conceptual framework to describe the likelihood of multi-step edits, and describe neural models that can learn a generative model of sequences based on these multistep edits. We introduce baseline results and metrics on this task, finding that modeling editing processes improves performance on a variety of axes on both our proposed task and related downstream tasks compared to previous single-step models of edits.- Anthology ID:
- 2022.findings-emnlp.280
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3822–3832
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.280
- DOI:
- 10.18653/v1/2022.findings-emnlp.280
- Cite (ACL):
- Machel Reid and Graham Neubig. 2022. Learning to Model Editing Processes. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3822–3832, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Learning to Model Editing Processes (Reid & Neubig, Findings 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2022.findings-emnlp.280.pdf