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

• URL: http://arxiv.org/abs/2103.07552v1
• Date: Fri, 12 Mar 2021 22:07:35 GMT
• Title: Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning
• Authors: Jason Wei, Chengyu Huang, Soroush Vosoughi, Yu Cheng, Shiqi Xu
• Abstract summary: Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories. This paper explores data augmentation -- a technique particularly suitable for training with limited data. We find that common data augmentation techniques can improve the performance of triplet networks by up to 3.0% on average.
• Score: 11.66053357388062
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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