Research on the Application of an OPT Model Integrating Meta-Learning and Prompt Learning for Few-Shot Event Extraction
X Li, B Azhati�- Proceedings of the 4th International Conference on�…, 2023 - dl.acm.org
X Li, B Azhati
Proceedings of the 4th International Conference on Artificial Intelligence�…, 2023•dl.acm.orgEvent extraction plays a pivotal role in natural language processing (NLP), especially in few-
shot learning environments where research is increasingly growing. This paper proposes an
OPT model that integrates Model-Agnostic Meta-Learning (MAML) and prompt learning to
enhance the performance of few-shot event extraction. Our method was tested on the
ACE2005 dataset and compared with existing models. The results demonstrate the
effectiveness of our approach in improving few-shot event extraction.
shot learning environments where research is increasingly growing. This paper proposes an
OPT model that integrates Model-Agnostic Meta-Learning (MAML) and prompt learning to
enhance the performance of few-shot event extraction. Our method was tested on the
ACE2005 dataset and compared with existing models. The results demonstrate the
effectiveness of our approach in improving few-shot event extraction.
Event extraction plays a pivotal role in natural language processing (NLP), especially in few-shot learning environments where research is increasingly growing. This paper proposes an OPT model that integrates Model-Agnostic Meta-Learning (MAML) and prompt learning to enhance the performance of few-shot event extraction. Our method was tested on the ACE2005 dataset and compared with existing models. The results demonstrate the effectiveness of our approach in improving few-shot event extraction.
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