A Two-Stage Active Learning Algorithm for NLP Based on Feature Mixing

Active learning (AL) aims to improve the model performance with minimal data annotation. While recent AL studies have utilized feature mixing to identify unlabeled instances with novel features, applying it to natural language processing (NLP) tasks has been challenged due to the discrete nature of text tokens and the limited contribution of some novel features. To address these issues, we propose a two-stage acquisition method based on feature mixing for NLP tasks. We first create a mixed feature for both labeled and unlabeled instances to identify the features in the unlabeled instances that the model cannot recognize. Next, we evaluate the contribution of these novel features to the model using the entropy of the nearest labeled neighbors. The proposed method enables the model to select the most informative samples in the unlabeled sample pool. Experiments on sentiment analysis, topic classification, and natural language inference validated that our method not only outperforms other AL approaches but improves the efficiency of batch data acquisition.