CLG: Contrastive Label Generation with Knowledge for Few-Shot Learning

Han Ma, Baoyu Fan, Benjamin K. Ng () and Chan-Tong Lam
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Han Ma: Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
Baoyu Fan: Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
Benjamin K. Ng: Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
Chan-Tong Lam: Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China

Mathematics, 2024, vol. 12, issue 3, 1-21

Abstract: Training large-scale models needs big data. However, the few-shot problem is difficult to resolve due to inadequate training data. It is valuable to use only a few training samples to perform the task, such as using big data for application scenarios due to cost and resource problems. So, to tackle this problem, we present a simple and efficient method, contrastive label generation with knowledge for few-shot learning (CLG). Specifically, we: (1) Propose contrastive label generation to align the label with data input and enhance feature representations; (2) Propose a label knowledge filter to avoid noise during injection of the explicit knowledge into the data and label; (3) Employ label logits mask to simplify the task; (4) Employ multi-task fusion loss to learn different perspectives from the training set. The experiments demonstrate that CLG achieves an accuracy of 59.237%, which is more than about 3% in comparison with the best baseline. It shows that CLG obtains better features and gives the model more information about the input sentences to improve the classification ability.

Keywords: few-shot learning; contrastive learning; knowledge graph; natural language processing; transfer learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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