@inproceedings{li-etal-2022-instilling,
title = "Instilling Type Knowledge in Language Models via Multi-Task {QA}",
author = "Li, Shuyang and
Sridhar, Mukund and
Satya Prakash, Chandana and
Cao, Jin and
Hamza, Wael and
McAuley, Julian",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.45",
doi = "10.18653/v1/2022.findings-naacl.45",
pages = "594--603",
abstract = "Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge{---}their \textit{types}.Previous methods to learn entity types rely on training classifiers on datasets with coarse, noisy, and incomplete labels. We introduce a method to instill fine-grained type knowledge in language models with text-to-text pre-training on type-centric questions leveraging knowledge base documents and knowledge graphs.We create the \textbf{WikiWiki} dataset: entities and passages from 10M Wikipedia articles linked to the Wikidata knowledge graph with 41K types.Models trained on WikiWiki achieve state-of-the-art performance in zero-shot dialog state tracking benchmarks, accurately infer entity types in Wikipedia articles, and can discover new types deemed useful by human judges.",
}
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<abstract>Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge—their types.Previous methods to learn entity types rely on training classifiers on datasets with coarse, noisy, and incomplete labels. We introduce a method to instill fine-grained type knowledge in language models with text-to-text pre-training on type-centric questions leveraging knowledge base documents and knowledge graphs.We create the WikiWiki dataset: entities and passages from 10M Wikipedia articles linked to the Wikidata knowledge graph with 41K types.Models trained on WikiWiki achieve state-of-the-art performance in zero-shot dialog state tracking benchmarks, accurately infer entity types in Wikipedia articles, and can discover new types deemed useful by human judges.</abstract>
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%0 Conference Proceedings
%T Instilling Type Knowledge in Language Models via Multi-Task QA
%A Li, Shuyang
%A Sridhar, Mukund
%A Satya Prakash, Chandana
%A Cao, Jin
%A Hamza, Wael
%A McAuley, Julian
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F li-etal-2022-instilling
%X Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge—their types.Previous methods to learn entity types rely on training classifiers on datasets with coarse, noisy, and incomplete labels. We introduce a method to instill fine-grained type knowledge in language models with text-to-text pre-training on type-centric questions leveraging knowledge base documents and knowledge graphs.We create the WikiWiki dataset: entities and passages from 10M Wikipedia articles linked to the Wikidata knowledge graph with 41K types.Models trained on WikiWiki achieve state-of-the-art performance in zero-shot dialog state tracking benchmarks, accurately infer entity types in Wikipedia articles, and can discover new types deemed useful by human judges.
%R 10.18653/v1/2022.findings-naacl.45
%U https://aclanthology.org/2022.findings-naacl.45
%U https://doi.org/10.18653/v1/2022.findings-naacl.45
%P 594-603
Markdown (Informal)
[Instilling Type Knowledge in Language Models via Multi-Task QA](https://aclanthology.org/2022.findings-naacl.45) (Li et al., Findings 2022)
ACL
- Shuyang Li, Mukund Sridhar, Chandana Satya Prakash, Jin Cao, Wael Hamza, and Julian McAuley. 2022. Instilling Type Knowledge in Language Models via Multi-Task QA. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 594–603, Seattle, United States. Association for Computational Linguistics.