Hong-Gee Kim


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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
Findings of the Association for Computational Linguistics: EMNLP 2022

As an effective approach to adapting pre-trained 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 prompt-learning 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 prompt-learning paradigm and further make a powerful alternative to vanilla fine-tuning.