Data Augmentation using Pre-trained Transformer Models
Abstract
Language model based pre-trained models such as BERT have provided significant gains across different NLP tasks. In this paper, we study different types of transformer based pre-trained models such as auto-regressive models (GPT-2), auto-encoder models (BERT), and seq2seq models (BART) for conditional data augmentation. We show that prepending the class labels to text sequences provides a simple yet effective way to condition the pre-trained models for data augmentation. Additionally, on three classification benchmarks, pre-trained Seq2Seq model outperforms other data augmentation methods in a low-resource setting. Further, we explore how different pre-trained model based data augmentation differs in-terms of data diversity, and how well such methods preserve the class-label information.
Cite
@article{arxiv.2003.02245,
title = {Data Augmentation using Pre-trained Transformer Models},
author = {Varun Kumar and Ashutosh Choudhary and Eunah Cho},
journal= {arXiv preprint arXiv:2003.02245},
year = {2021}
}
Comments
In Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems @ AACL 2020; Code: https://github.com/varinf/TransformersDataAugmentation