Reinforced Co-Training
This addresses the issue of inefficient unlabeled data utilization in semi-supervised learning for text classification, though it is an incremental improvement over existing co-training methods.
The paper tackles the problem of sample selection bias in co-training for semi-supervised learning by proposing Reinforced Co-Training, which uses Q-learning to learn a data selection policy, resulting in more accurate text classification on clickbait detection and generic tasks.
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.