Prompt-learning for Fine-grained Entity Typing
Ning Ding, Yulin Chen, Xu Han, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Haitao Zheng, Zhiyuan Liu, Juanzi Li, Hong‐Gee Kim · 2022
As an effective approach to adapting pretrained language models (PLMs) for specific tasks, prompt-learning has recently attracted much attention from researchers.By using cloze-style language prompts to stimulate the versatile knowledge of PLMs, prompt-learning can achieve promising results on a series of NLP tasks, such as natural language inference, sentiment classification, and knowledge probing.In this work, we investigate the application of prompt-learning on fine-grained entity typing in fully supervised, few-shot and zero-shot scenarios.We first develop a simple and effective prompt-learning pipeline by constructing entity-oriented verbalizers and templates and conducting masked language modeling.Further, to tackle the zero-shot regime, we propose a self-supervised strategy that carries out distribution-level optimization in promptlearning to automatically summarize the information of entity types.Extensive experiments on four fine-grained entity typing benchmarks under fully supervised, few-shot, and zero-shot settings show the effectiveness of the promptlearning paradigm and further make a powerful alternative to vanilla fine-tuning.