论文标题
对自然语言处理积极学习的调查
A Survey of Active Learning for Natural Language Processing
论文作者
论文摘要
在这项工作中，我们为其在自然语言处理（NLP）中应用的主动学习（AL）提供了一项调查。除了对查询策略的细粒度分类外，我们还研究了将AL应用于NLP问题的其他一些重要方面。其中包括用于结构化预测任务，注释成本，模型学习（尤其是具有深层神经模型）以及开始和停止AL的AL。最后，我们以讨论相关主题和未来方向的讨论来结束。
In this work, we provide a survey of active learning (AL) for its applications in natural language processing (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.
