Zero-shot Text Classification via Reinforced Self-training

By Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, Suhang Zheng, Feng Wang, Jun Zhang, Huajun Chen. 2020

80 citations recorded by the source corpus.

Zero-shot learning has been a tough problem since no labeled data is available for unseen classes during training, especially for classes with low similarity. In this situation, transferring from seen classes to unseen classes is extremely hard. To tackle this problem, in this paper we propose a self-training based method to efficiently leverage unlabeled data. Traditional self-training methods use fixed heuristics to select instances from unlabeled data, whose performance varies among different datasets. We propose a reinforcement learning framework to learn data selection strategy automatically and provide more reliable selection. Experimental results on both benchmarks and a real-world e-commerce dataset show that our approach significantly outperforms previous methods in zero-shot text classification

Subfields: Natural Language Processing, Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

  1. Hinrich Schütze
  2. Timo Schick
  3. Jiwei Li
  4. Fei Wu
  5. Xiaofei Sun
  6. Qinghong Han
  7. Yuxian Meng
  8. Minlie Huang

In Reinforcement Learning

  1. Fei Wu
  2. Jiwei Li
  3. Qinghong Han

Citations in the corpus