Improving Distantly Supervised Relation Extraction by Natural Language Inference
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 provide indirect supervision and further consolidates the distant annotations to benefit multi-classification RE models. The NLI-based indirect supervision acquires only one relation verbalization template from humans as a semantically general template for each relationship, and then the template set is enriched by high-quality textual patterns automatically mined from the distantly annotated corpus. With two simple and effective data consolidation strategies, the quality of training data is substantially improved. Extensive experiments demonstrate that the proposed framework significantly improves the SOTA performance (up to 7.73\% of F1) on distantly supervised RE benchmark datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
Natural Language InferenceRelationRelation ExtractionSimilar Papers 제목 키워드 기반
Improving Distantly Supervised Document-Level Relation Extraction Through Natural Language Inference
t
Document-level Relation ExtractionNatural Language InferenceRelationRelation ExtractionDeep Residual Learning for Weakly-Supervised Relation Extraction
Deep residual learning (ResNet) is a new method for training very deep neural networks using identity map-ping for shortcut connections. ResNet has won the ImageNet ILSVRC 2015 classification task, and achieved state-of-…
General ClassificationRelationRelation ExtractionBootstrapping Distantly Supervised IE using Joint Learning and Small Well-structured Corpora
We propose a framework to improve performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction. We combine this with a novel…
RelationRelation ExtractionActive Testing: An Unbiased Evaluation Method for Distantly Supervised Relation Extraction
Distant supervision has been a widely used method for neural relation extraction for its convenience of automatically labeling datasets. However, existing works on distantly supervised relation extraction suffer from the…
RelationRelation ExtractionFine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction
Distantly supervised relation extraction is widely used to extract relational facts from text, but suffers from noisy labels. Current relation extraction methods try to alleviate the noise by multi-instance learning and …
Common Sense ReasoningLanguage ModellingRelationRelation Classification+1