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Improving Distantly Supervised Relation Extraction by Natural Language Inference
[article]
2022
arXiv
pre-print
To reduce human annotations for relation extraction (RE) tasks, distantly supervised approaches have been proposed, while struggling with low performance. In this work, we propose a novel DSRE-NLI framework, which considers both distant supervision from existing knowledge bases and indirect supervision from pretrained language models for other tasks. DSRE-NLI energizes an off-the-shelf natural language inference (NLI) engine with a semi-automatic relation verbalization (SARV) mechanism to
arXiv:2208.00346v1
fatcat:nzakmtb73vcdfhaxuxnmnannta