计算机科学
人工智能
自然语言处理
元学习(计算机科学)
自然(考古学)
自然语言
弹丸
机器学习
任务(项目管理)
工程类
历史
考古
有机化学
化学
系统工程
出处
期刊:International Conference on Artificial Intelligence
日期:2021-05-01
被引量:7
标识
DOI:10.1109/aiea53260.2021.00069
摘要
The annotated dataset is the foundation for Supervised Natural Language Processing. However, the cost of obtaining dataset is high. In recent years, the Few-Shot Learning has gradually attracted the attention of researchers. From the definition, in this paper, we conclude the difference in Few-Shot Learning between Natural Language Processing and Computer Vision. On that basis, the current Few-Shot Learning on Natural Language Processing is summarized, including Transfer Learning, Meta Learning and Knowledge Distillation. Furthermore, we conclude the solutions to Few-Shot Learning in Natural Language Processing, such as the method based on Distant Supervision, Meta Learning and Knowledge Distillation. Finally, we present the challenges facing Few-Shot Learning in Natural Language Processing.
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