Few-Shot Text Classification with Global-Local Feature Information

Meta-learning frameworks have been proposed to generalize machine learning models for domain adaptation without sufficient label data in computer vision. However, text classification with meta-learning is less investigated. In this paper, we propose SumFS to find global top-ranked sentences by extractive summary and improve the local vocabulary category features. The SumFS consists of three modules: (1) an unsupervised text summarizer that removes redundant information; (2) a weighting generator that associates feature words with attention scores to weight the lexical representations of words; (3) a regular meta-learning framework that trains with limited labeled data using a ridge regression classifier. In addition, a marine news dataset was established with limited label data. The performance of the algorithm was tested on THUCnews, Fudan, and marine news datasets. Experiments show that the SumFS can maintain or even improve accuracy while reducing input features. Moreover, the training time of each epoch is reduced by more than 50%.

Automated summary

In this paper, the authors propose SumFS, a meta-learning framework for text classification that utilizes extractive summarization and improved local vocabulary features to achieve domain adaptation with limited labeled data. Experimental results show that SumFS can reduce input features while maintaining or improving accuracy, and significantly decrease training time.