FREDA: Few-Shot Relation Extraction Based on Data Augmentation

Liu, Junbao; Qin, Xizhong; Ma, Xiaojing; Ran, Wensheng

doi:10.3390/app13148312

Cited by 1 publication

(1 citation statement)

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“…To deal with the few-shot problem, Ref. [48] uses triplet information for data augmentation in relationship extraction, Ref. [49] uses clustering algorithms to construct pseudo-labeled data to alleviate the problem of insufficient sample size, and Ref.…”

Section: Related Workmentioning

confidence: 99%

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

Ma,

Fan,

et al. 2024

Mathematics

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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.

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Section: Related Workmentioning

confidence: 99%