How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?
This work addresses the problem of optimizing data augmentation for NLP practitioners using pretrained models, revealing that common techniques may be ineffective, which is incremental as it builds on prior studies but provides a systematic negative result.
The paper investigated the effectiveness of task-gnostic data augmentation techniques, specifically Easy Data Augmentation and Back-Translation, on pretrained transformers like BERT, XLNet, and RoBERTa across multiple classification tasks and datasets, finding that these methods do not consistently improve performance even in low-data regimes.
Task-agnostic forms of data augmentation have proven widely effective in computer vision, even on pretrained models. In NLP similar results are reported most commonly for low data regimes, non-pretrained models, or situationally for pretrained models. In this paper we ask how effective these techniques really are when applied to pretrained transformers. Using two popular varieties of task-agnostic data augmentation (not tailored to any particular task), Easy Data Augmentation (Wei and Zou, 2019) and Back-Translation (Sennrichet al., 2015), we conduct a systematic examination of their effects across 5 classification tasks, 6 datasets, and 3 variants of modern pretrained transformers, including BERT, XLNet, and RoBERTa. We observe a negative result, finding that techniques which previously reported strong improvements for non-pretrained models fail to consistently improve performance for pretrained transformers, even when training data is limited. We hope this empirical analysis helps inform practitioners where data augmentation techniques may confer improvements.