@inproceedings{ding-etal-2022-prompt,
title = "Prompt-learning for Fine-grained Entity Typing",
author = "Ding, Ning and
Chen, Yulin and
Han, Xu and
Xu, Guangwei and
Wang, Xiaobin and
Xie, Pengjun and
Zheng, Haitao and
Liu, Zhiyuan and
Li, Juanzi and
Kim, Hong-Gee",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.512",
doi = "10.18653/v1/2022.findings-emnlp.512",
pages = "6888--6901",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Prompt-learning for Fine-grained Entity Typing
%A Ding, Ning
%A Chen, Yulin
%A Han, Xu
%A Xu, Guangwei
%A Wang, Xiaobin
%A Xie, Pengjun
%A Zheng, Haitao
%A Liu, Zhiyuan
%A Li, Juanzi
%A Kim, Hong-Gee
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F ding-etal-2022-prompt
%X 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.
%R 10.18653/v1/2022.findings-emnlp.512
%U https://aclanthology.org/2022.findings-emnlp.512
%U https://doi.org/10.18653/v1/2022.findings-emnlp.512
%P 6888-6901
Markdown (Informal)
[Prompt-learning for Fine-grained Entity Typing](https://aclanthology.org/2022.findings-emnlp.512) (Ding et al., Findings 2022)
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.