Biblio

The annotated dataset is the foundation for Supervised Natural Language Processing. However, the cost of obtaining dataset is high. In recent years, the Few-Shot Learning has gradually attracted the attention of researchers. From the definition, in this paper, we conclude the difference in Few-Shot Learning between Natural Language Processing and Computer Vision. On that basis, the current Few-Shot Learning on Natural Language Processing is summarized, including Transfer Learning, Meta Learning and Knowledge Distillation. Furthermore, we conclude the solutions to Few-Shot Learning in Natural Language Processing, such as the method based on Distant Supervision, Meta Learning and Knowledge Distillation. Finally, we present the challenges facing Few-Shot Learning in Natural Language Processing.

Imbalanced big data means big data where the ratio of a certain class is relatively small compared to other classes. When the machine learning model is trained by using imbalanced big data, the problem with performance drops for the minority class occurs. For this reason, various oversampling methodologies have been proposed, but simple oversampling leads to problem of the overfitting. In this paper, we propose a meta learning methodology for efficient analysis of imbalanced big data. The proposed meta learning methodology uses the meta information of the data generated by the generative model based on Generative Adversarial Networks. It prevents the generative model from becoming too similar to the real data in minority class. Compared to the simple oversampling methodology for analyzing imbalanced big data, it is less likely to cause overfitting. Experimental results show that the proposed method can efficiently analyze imbalanced big data.