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Augmenting Data With Mixup For Sentence Classification: An Empirical Study

Hongyu Guo, Yongyi Mao, Richong Zhang . Arxiv 2019 – 153 citations

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Compositional Generalization Content Enrichment Datasets Evaluation Image Text Integration Interactive Environments Interdisciplinary Approaches Model Architecture Multimodal Semantic Representation Neural Machine Translation Productivity Enhancement Question Answering Visual Contextualization

Mixup, a recent proposed data augmentation method through linearly interpolating inputs and modeling targets of random samples, has demonstrated its capability of significantly improving the predictive accuracy of the state-of-the-art networks for image classification. However, how this technique can be applied to and what is its effectiveness on natural language processing (NLP) tasks have not been investigated. In this paper, we propose two strategies for the adaption of Mixup on sentence classification: one performs interpolation on word embeddings and another on sentence embeddings. We conduct experiments to evaluate our methods using several benchmark datasets. Our studies show that such interpolation strategies serve as an effective, domain independent data augmentation approach for sentence classification, and can result in significant accuracy improvement for both CNN and LSTM models.

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