FPrompt-PLM: Flexible-Prompt on Pretrained Language Model for Continual Few-Shot Relation Extraction

Lingling Zhang, Yifei Li, Qianying Wang, Yun Wang, Hang Yan, Jiaxin Wang, Jun Liu

Abstract: Relation extraction (RE) aims to identify the relation between two entities within a sentence, which plays a crucial role in information extraction. Traditional supervised setting on RE does not fit the actual scenario, due to the continuous emergence of new relations and the unavailability of massive labeled examples. Continual few-shot relation extraction (CFS-RE) is proposed as a potential solution to the above situation, which requires the model to learn new relations sequentially from a few examples. Apparently, CFS-RE is more challenging than previous RE, as the catastrophic forgetting of old knowledge and few-shot overfitting on a handful of examples. To this end, we propose a novel flexible-prompt framework on pretrained language model named FPrompt-PLM for CFS-RE, which includes flexible-prompt embedding, pretrained-language understanding, and nearest-prototype learning modules. Note that two pools in FPrompt-PLM, i.e., prompt and prototype pools, are continual updated and applied for prediction of all seen relations at current time-step. The former pool records the distinctive prompt embedding in each time period, and the latter records all learned relation prototypes. Besides, three progressive stages are introduced to learn FPrompt-PLM's parameters and apply this model for CFS-RE testing, which includes meta-training, continual meta-finetuning, and testing stages. And we improve the CFS-RE loss by incorporating multiple distillation losses as well as a novel prototype-diversity loss in these stages to alleviate the catastrophic forgetting and few-shot overfitting problems. Comprehensive experiments on two widely-used datasets show that FPrompt-PLM achieves significant performance improvements over the SOTA baselines.