Extracting Few-Shot Relation Based on Prompt Ensemble
[Objective]This paper addresses the challenge of constructing label mapping in prompt learning-based relation extraction methods when labeled data is scarce.[Methods]The proposed approach enhances prompt effectiveness by injecting relational semantics into the prompt template.Data augmentation is performed through prompt ensemble,and an instance-level attention mechanism is used to extract important features during the prototype construction process.[Results]On the public FewRel dataset,the accuracy of the proposed method surpasses the baseline model by 2.13％,0.55％,1.40％,and 2.91％in four few-shot test scenarios,respectively.[Limitations]The method does not utilize learnable virtual prompt templates in constructing prompt templates,and there is still room for improvement in the representation of answer words.[Conclusions]The proposed method effectively mitigates the problem of limited information and insufficient accuracy in prototype construction under few-shot scenarios,improving the model's accuracy in few-shot relation extraction tasks.
NETL
NSTL
万方数据
