Theodoros Rekatsinas


2020

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Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion
Ankur Goswami | Akshata Bhat | Hadar Ohana | Theodoros Rekatsinas
Findings of the Association for Computational Linguistics: EMNLP 2020

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.