Augmenting Data with Mixup for Sentence Classification: An Empirical Study
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.
Code (3)
Tasks
ClassificationData AugmentationGeneral Classificationimage-classificationImage ClassificationSentenceSentence ClassificationSentence EmbeddingsWord EmbeddingsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SSMix: Saliency-Based Span Mixup for Text Classification
Data augmentation with mixup has shown to be effective on various computer vision tasks. Despite its great success, there has been a hurdle to apply mixup to NLP tasks since text consists of discrete tokens with variable…
ClassificationData AugmentationNatural Language InferenceSentence+4Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup
Mixup is a highly successful technique to improve generalization of neural networks by augmenting the training data with combinations of random pairs. Selective mixup is a family of methods that apply mixup to specific p…
Binary ClassificationDMix: Distance Constrained Interpolative Mixup
Interpolation-based regularisation methods have proven to be effective for various tasks and modalities. Mixup is a data augmentation method that generates virtual training samples from convex combinations of individual …
ClassificationData AugmentationSentenceSentence ClassificationTowards Understanding the Data Dependency of Mixup-style Training
In the Mixup training paradigm, a model is trained using convex combinations of data points and their associated labels. Despite seeing very few true data points during training, models trained using Mixup seem to still …
LINDA: Unsupervised Learning to Interpolate in Natural Language Processing
Despite the success of mixup in data augmentation, its applicability to natural language processing (NLP) tasks has been limited due to the discrete and variable-length nature of natural languages. Recent studies have th…
Data Augmentationtext-classificationText Classification