Commonsense Knowledge Mining from Pretrained Models
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Joe Davison, Joshua Feldman, Alexander Rush. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
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Davison et al. investigate knowledge base completion and design a template for an input head-relation-tail triple using LMs, scoring filled prompts with a unidirectional LM and selecting the one with highest LM probability.
“Davison et al. [19] investigate the task of knowledge base completion and design a template for an input (head-relation-tail triple) using LMs.They first handcraft a set of templates as potential candidates and fill the input and answer slots to form a filled prompt.They then use a unidirectional LM to score those filled prompts, selecting the one with the highest LM probability.”
From Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2023) · cites this paper as [19]
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Cites
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“…et al., 2019; Peters et al., 2018; Raffel et al., 2020; Liu et al., 2019; Yang et al., 2019; Radford et al., 2019) have shown remarkable performance when conditioned with an appropriate textual context (Petroni et al., 2019, 2020; Jiang et al., 2020; Shin et al., 2020; Davison et al., 2019).”
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“[19] investigate the task of knowledge base completion and design a template for an input (head-relation-tail triple) using LMs.”
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“…et al., 2019; Peters et al., 2018; Raffel et al., 2020; Liu et al., 2019; Yang et al., 2019; Radford et al., 2019) have shown remarkable performance when conditioned with an appropriate textual context (Petroni et al., 2019, 2020; Jiang et al., 2020; Shin et al., 2020; Davison et al., 2019).”
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“ries of research work on knowledge probing (Trinh and Le, 2018; Petroni et al., 2019; Davison et al., 2019), language prompts have been proven to effectively stimulate knowledge from PLMs.”
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- Authors
- Davison, J., Feldman, J., & Rush, A.
- Affiliations
- Harvard University (US)
- ORCID
- Rush, Alexander
- Venue
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Published
- 2019
- Pages
- 1173-1178
- OpenAlex
- W2970161131
- Cited by
- 264
- References
- 21
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 32 · 2023: 57 · 2022: 58 · 2021: 65 · 2020: 31 · 2019: 2
- FWCI
- 14.92
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- National Science Foundation (1845664)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- commonsense knowledge, knowledge mining, pre-trained language models, knowledge extraction, commonsense reasoning
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