Computer Science > Computation and Language
[Submitted on 11 Apr 2021 (v1), last revised 29 Nov 2021 (this version, v3)]
Title:Constructing Contrastive samples via Summarization for Text Classification with limited annotations
View PDFAbstract:Contrastive Learning has emerged as a powerful representation learning method and facilitates various downstream tasks especially when supervised data is limited. How to construct efficient contrastive samples through data augmentation is key to its success. Unlike vision tasks, the data augmentation method for contrastive learning has not been investigated sufficiently in language tasks. In this paper, we propose a novel approach to construct contrastive samples for language tasks using text summarization. We use these samples for supervised contrastive learning to gain better text representations which greatly benefit text classification tasks with limited annotations. To further improve the method, we mix up samples from different classes and add an extra regularization, named Mixsum, in addition to the cross-entropy-loss. Experiments on real-world text classification datasets (Amazon-5, Yelp-5, AG News, and IMDb) demonstrate the effectiveness of the proposed contrastive learning framework with summarization-based data augmentation and Mixsum regularization.
Submission history
From: Yangkai Du [view email][v1] Sun, 11 Apr 2021 20:13:24 UTC (86 KB)
[v2] Fri, 10 Sep 2021 05:49:56 UTC (106 KB)
[v3] Mon, 29 Nov 2021 07:20:41 UTC (106 KB)
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