| Title | A Survey on Few-Shot Learning in Natural Language Processing |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Yang, Mengde |
| Conference Name | 2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) |
| Keywords | Automation, Computer vision, Costs, distant supervision, Few-shot learning, Human Behavior, learning (artificial intelligence), Meta Learning, natural language processing, pubcrawl, resilience, Resiliency, Scalability, Systematics, transfer learning |
| Abstract | 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. |
| DOI | 10.1109/AIEA53260.2021.00069 |
| Citation Key | yang_survey_2021 |
A Survey on Few-Shot Learning in Natural Language Processing