# Fast Cross-domain Data Augmentation through Neural Sentence Editing

arXiv: https://arxiv.org/abs/2003.10254 · HTML: https://mlsanity.com/paper/2003.10254/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [Missingness Augmentation: A General Approach for Improving Generative Imputation Models](https://mlsanity.com/paper/2108.02566/) (2021)
- [Annotated Dataset Creation through General Purpose Language Models for non-English Medical NLP](https://mlsanity.com/paper/2208.14493/) (2022)
- [Investigating Masking-based Data Generation in Language Models](https://mlsanity.com/paper/2307.00008/) (2023)

## Abstract (arXiv)

Data augmentation promises to alleviate data scarcity. This is most important in cases where the initial data is in short supply. This is, for existing methods, also where augmenting is the most difficult, as learning the full data distribution is impossible. For natural language, sentence editing offers a solution - relying on small but meaningful changes to the original ones. Learning which changes are meaningful also requires large amounts of training data. We thus aim to learn this in a source domain where data is abundant and apply it in a different, target domain, where data is scarce - cross-domain augmentation. We create the Edit-transformer, a Transformer-based sentence editor that is significantly faster than the state of the art and also works cross-domain. We argue that, due to its structure, the Edit-transformer is better suited for cross-domain environments than its edit-based predecessors. We show this performance gap on the Yelp-Wikipedia domain pairs. Finally, we show that due to this cross-domain performance advantage, the Edit-transformer leads to meaningful performance gains in several downstream tasks.