Reinforced Co-Training

TL;DR

Reinforced Co-Training introduces a Q-learning based approach to improve sample selection in semi-supervised learning, leading to more accurate text classification by better utilizing unlabeled data.

Contribution

The paper presents a novel reinforcement learning framework for sample selection in co-training, addressing bias and exploration issues in traditional methods.

Findings

Improved classification accuracy on clickbait detection.

Enhanced performance on generic text classification tasks.

Effective automatic data selection policy learned via Q-learning.

Abstract

Co-training is a popular semi-supervised learning framework to utilize a large amount of unlabeled data in addition to a small labeled set. Co-training methods exploit predicted labels on the unlabeled data and select samples based on prediction confidence to augment the training. However, the selection of samples in existing co-training methods is based on a predetermined policy, which ignores the sampling bias between the unlabeled and the labeled subsets, and fails to explore the data space. In this paper, we propose a novel method, Reinforced Co-Training, to select high-quality unlabeled samples to better co-train on. More specifically, our approach uses Q-learning to learn a data selection policy with a small labeled dataset, and then exploits this policy to train the co-training classifiers automatically. Experimental results on clickbait detection and generic text classification tasks demonstrate that our proposed method can obtain more accurate text classification results.