Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning (2021)

Abstract: Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category. This paper explores data augmentation—a technique particularly suitable for training with limited data—for this few-shot, highly-multiclass text classification setting. On four diverse text classification tasks, we find that common data augmentation techniques can improve the performance of triplet networks by up to 3.0% on average. To further boost performance, we present a simple training strategy called curriculum data augmentation, which leverages curriculum learning by first training on only original examples and then introducing augmented data as training progresses. We explore a two-stage and a gradual schedule, and find that, compared with standard single-stage training, curriculum data augmentation trains faster, improves performance, and remains robust to high amounts of noising from augmentation.

## introduction traditional text classification tasks such as sentiment classification @xcite typically have few output classes (e.g., in binary classification), each with many training examples. many practical scenarios such as relation classification @xcite , answer selection @xcite , and sentence clustering @xcite , however, have a converse setup characterized by a large number of output classes @xcite , often with few training examples per class. this scenario, which we henceforth refer to as few-shot, highly-multiclass text classification, is a common setting in nlp applications and can be challenging due to the scarcity of training data. data augmentation for nlp has seen increased interest in recent years @xcite ; qiu et al., 2020). in traditional text classification tasks, it has been shown that although performance improvements can be marginal when training data is sufficient, augmentation is especially beneficial in limited data scenarios @xcite . as such, we hypothesize that the few-shot, highly-multiclass text classification scenario is a suitable context for data augmentation. based on this motivation, our paper makes two main contributions. ## curriculum data augmentation motivation. inspired by human and animal learning, curriculum learning @xcite posits that neural networks train better when examples are not randomly presented but instead organized in a meaningful order that gradually shows more concepts and complexity. traditionally, curriculum learning approaches first assume that a range of example difficulty exists in the data and then leverage various heuristics to sort examples by difficulty and train models on progressively harder examples @xcite @xcite . a newer school of thought, however, has noted that instead of discovering a curriculum in existing data, data can be intentionally modified to dictate an artificial range of difficulty @xcite this is the approach we will take here. our approach. unlike data augmentation in computer vision where augmented data undoubtedly resembles original data, in text, data augmentation techniques might introduce linguistic adversity and therefore can be seen as a form of noising @xcite , where noised data is harder to learn from than unmodified original data. as such, we can create an artificial curriculum in the data by leveraging controlled application of data augmentation, starting by training on only original data and then adding augmented data with a higher levels of noising as training progresses ## experimental setup we conduct empirical experiments to evaluate curriculum data augmentation on a variety of text classification tasks using a triplet loss model. foot_0 ## related work and conclusions our work combines curriculum learning, data augmentation, and triplet loss, and is inspired by prior work in these areas. in vision, several papers have proposed reinforcement learning policies for data augmentation @xcite , and hard negative mining @xcite itself can be seen as a form of curriculum learning. in nlp, the work of @xcite is perhaps most similar to ours-they show that sampling strategies are key for improving performance with triplet loss networks. we see our work as the first to explicitly analyze curriculum learning for data augmentation in text. in closing, we have proposed a curriculum data augmentation framework that is simple yet provides empirical performance improvements, a compelling case for the combination of ideas explored. our approach exemplifies how data augmentation can create an artificial range of example difficulty that is helpful for curriculum learning, a direction that potentially warrants future research.