F lip DA : Effective and Robust Data Augmentation for Few-Shot Learning (2022)

Abstract: Most previous methods for text data augmentation are limited to simple tasks and weak baselines. We explore data augmentation on hard tasks (i.e., few-shot natural language understanding) and strong baselines (i.e., pretrained models with over one billion parameters). Under this setting, we reproduced a large number of previous augmentation methods and found that these methods bring marginal gains at best and sometimes degrade the performance much. To address this challenge, we propose a novel data augmentation method FlipDA that jointly uses a generative model and a classifier to generate label-flipped data. Central to the idea of FlipDA is the discovery that generating label-flipped data is more crucial to the performance than generating label-preserved data. Experiments show that FlipDA achieves a good tradeoff between effectiveness and robustness—it substantially improves many tasks while not negatively affecting the others.

## introduction data augmentation is a method to augment the training set by generating new data from the given data. for text data, basic operations including replacement, insertion, deletion, and shuffle have been adopted widely and integrated into a wide range of augmentation frameworks @xcite @xcite @xcite . generative modeling methods such as back-translation have also been employed to generate augmented samples @xcite . however, there are two major limitations. first, some general augmentation methods are based on weak baselines without using large-scale pretrained language models. recent work showed that some of the data augmentation methods are less useful when combined with large pretrained models @xcite . second, most prior studies are carried on simple tasks such as single-sentence classification where it is easier to generate legit augmented samples. for harder tasks such as natural language inference (e.g., telling whether sentence a entails sentence b), it is not clear whether previous methods still help. this work takes a step further to study data augmentation under strong baselines and hard tasks. our study employs large-scale pretrained language models such as deberta @xcite with over one billion parameters as baselines. moreover, we target a very challenging setting-fewshot natural language understanding (nlu). following @xcite , we consider challenging nlu tasks including question answering, textual entailment, coreference resolution, and word sense disambiguation, and use only 32 training examples for each task. under this setting, we reproduced several widely-used prior methods for data augmentation. our experiments lead to two unexpected discoveries: (1) most of prior augmentation methods bring only marginal gains at best and are not effective for most tasks; (2) in many cases, using data augmentation results in instability in performance and even entering a failure mode; i.e., performance may drop by a lot or fluctuate severely depending on which pretrained model is used. the above issues prevent these augmentation methods from practical usage for few-shot learning. we propose a novel method flipda that achieves both effectiveness and robustness for hard few-shot tasks. preliminary experiments showed that labelflipped data often largely improve the generalization of pretrained models, compared to augmented data that preserve the original labels. based on this observation, flipda first generates data using word substitution based on a pretrained t5 @xcite and uses a classifier to select label-flipped data. experiments demonstrate flipda substantially improves performance on many of the hard tasks, outperforming previous augmentation baselines in terms of average performance by a large margin. moreover, flipda is robust across different pretrained models and different tasks, avoiding failure modes. some augmentation methods are based on the perturbation in the feature space @xcite @xcite @xcite . generation @xcite @xcite @xcite based methods are also proposed for better data diversity. in addition, large pretrained models have been used for data augmentation. @xcite utilize large pretrained models, such as gpt-2, bert, and bart, for conditional data augmentation. lambada @xcite finetunes a gpt-2 model with the priming technique to get augmented examples. gpt3mix @xcite uses gpt-3 along with prompting to generate augmented data for classification tasks. our method is similar to this line of work in that we also use pretrained models for generating augmented data. however, there are the following key differences. first, it is challenging for these prior methods to handle long sequences or multiple sentences. in our preliminary experiments, we were not able to use these methods to generate proper data samples (see details in section 4). second, besides generating augmented samples, we found it crucial to use label-flipped data for augmentation, which is a unique and critical aspect of flipda. self-training. self-training @xcite iteratively augments training data by labeling unlabeled data with a trained model @xcite . knowledge distillation and pseudo-labeling are special forms of self-training @xcite @xcite . strong data augmentation @xcite , equal-orlarger model @xcite , additional noise @xcite , and feedback of the student's performance @xcite are helpful for self-training. self-training bears similarity to the second phase of flipda where a teacher model is used to filter samples. different from self-training, flipda leverages the advantages of label flipping to improve performance and does not rely on unlabeled data. ## few-shot data augmentation large-scale pretrained models. our setting assumes a large-scale pretrained language model @xcite @xcite ) is available and few-shot learning is performed based on the pretrained model. this setting is crucial since previous studies found that using a strong pretrained model as the baseline eliminates the benefits of data augmentation @xcite while large pretrained models are becoming more and more available. our main result is based on deberta @xcite with over one billion parameters. we also provide results with albert which has fewer parameters @xcite . ## conclusions we propose to study few-shot nlu based on largescale pretrained models. two key desiderata, i.e., effectiveness and robustness, are identified. based on the empirical insight that label flipping improves few-shot generalization, we propose flipda with automatic label flipping and data selection. experiments demonstrate the superiority of flipda, outperforming previous methods in terms of both effectiveness and robustness. in the future, it will be crucial to theoretically understand why and how generating label-flipped data in the neighborhood of existing data points improves generalization. moreover, increasing the diversity and quality of augmented data generation is also an important long-term goal.