Instilling Type Knowledge in Language Models via Multi-Task QA
Shuyang Li, Mukund Sridhar, Chandana Satya Prakash, Jin Cao, Wael Hamza, Julian McAuley
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.- Anthology ID:
- 2022.findings-naacl.45
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 594–603
- Language:
- URL:
- https://aclanthology.org/2022.findings-naacl.45
- DOI:
- 10.18653/v1/2022.findings-naacl.45
- Cite (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.
- Cite (Informal):
- Instilling Type Knowledge in Language Models via Multi-Task QA (Li et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.findings-naacl.45.pdf
- Code
- amazon-research/wikiwiki-dataset
- Data
- WikiWiki