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


Abstract
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.
Anthology ID:
2022.findings-emnlp.512
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6888–6901
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.512
DOI:
10.18653/v1/2022.findings-emnlp.512
Bibkey:
Cite (ACL):
Ning Ding, Yulin Chen, Xu Han, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Haitao Zheng, Zhiyuan Liu, Juanzi Li, and Hong-Gee Kim. 2022. Prompt-learning for Fine-grained Entity Typing. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6888–6901, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Prompt-learning for Fine-grained Entity Typing (Ding et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.512.pdf