  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.
