Looking Back on the Past: Active Learning With Historical Evaluation Results

Active learning is an effective approach for tasks with limited labeled data. It samples a small set of data to annotate actively and is widely applied in various AI tasks. It uses an iterative process, during which we utilize the current trained model to evaluate all unlabeled samples and annotate the best samples based on a specific query strategy to update the underlying model iteratively. Most existing active learning approaches rely on only the evaluation results generated by the current model and ignore the results from previous iterations. In this paper, we propose using more historical evaluation results which can provide additional information to help better select samples. First, we apply two kinds of heuristic features of the historical evaluation results, the weighted sum of and fluctuation of the historical evaluation sequence, to improve the effectiveness of sampling. Next, to use the information contained in the historical results more globally, we design a novel query strategy that learns how to select samples based on the historical sequences automatically. We also improve current state-of-the-art active learning methods by introducing historical evaluation results. Experimental results on two common NLP tasks including text classification and named entity recognition show that our methods significantly outperform current methods