A Survey of Active Learning for Natural Language Processing

Abstract: In this work, we provide a survey of active learning (AL) for its applications in NLP. In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions.