Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion
Ankur Goswami, Akshata Bhat, Hadar Ohana, Theodoros Rekatsinas
Abstract
We show that state-of-the-art self-supervised language models can be readily used to extract relations from a corpus without the need to train a fine-tuned extractive head. We introduce RE-Flex, a simple framework that performs constrained cloze completion over pretrained language models to perform unsupervised relation extraction. RE-Flex uses contextual matching to ensure that language model predictions matches supporting evidence from the input corpus that is relevant to a target relation. We perform an extensive experimental study over multiple relation extraction benchmarks and demonstrate that RE-Flex outperforms competing unsupervised relation extraction methods based on pretrained language models by up to 27.8 F1 points compared to the next-best method. Our results show that constrained inference queries against a language model can enable accurate unsupervised relation extraction.- Anthology ID:
- 2020.findings-emnlp.113
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1263–1276
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.113
- DOI:
- 10.18653/v1/2020.findings-emnlp.113
- Cite (ACL):
- Ankur Goswami, Akshata Bhat, Hadar Ohana, and Theodoros Rekatsinas. 2020. Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1263–1276, Online. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion (Goswami et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.113.pdf
- Data
- LAMA, SQuAD, T-REx