@inproceedings{goswami-etal-2020-unsupervised-relation,
title = "Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion",
author = "Goswami, Ankur and
Bhat, Akshata and
Ohana, Hadar and
Rekatsinas, Theodoros",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.113",
doi = "10.18653/v1/2020.findings-emnlp.113",
pages = "1263--1276",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion
%A Goswami, Ankur
%A Bhat, Akshata
%A Ohana, Hadar
%A Rekatsinas, Theodoros
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F goswami-etal-2020-unsupervised-relation
%X 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.
%R 10.18653/v1/2020.findings-emnlp.113
%U https://aclanthology.org/2020.findings-emnlp.113
%U https://doi.org/10.18653/v1/2020.findings-emnlp.113
%P 1263-1276
Markdown (Informal)
[Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion](https://aclanthology.org/2020.findings-emnlp.113) (Goswami et al., Findings 2020)
ACL