Reliable Data Generation and Selection for Low-Resource Relation Extraction
DOI:
https://doi.org/10.1609/aaai.v38i17.29915Keywords:
NLP: Information Extraction, NLP: GenerationAbstract
Automated construction of annotated data holds significant importance in Relation Extraction (RE) tasks due to the hardness and cost of human annotation. In this work, we propose Self-RDGS, a method for Self-supervised Reliable Data Generation and Selection in low-resource RE tasks. At first, we fully utilize the knowledge of triplets as prompts to generate sentences by employing the Large Language Models (LLMs). Since the auto-generated data contains noise, we then propose a ranking-based data selection method to select reliable sentences. Finally, we integrate the data selection and RE model training within a self-supervised iterative framework. Through experimentation on three datasets with low-resource settings, we demonstrate the effectiveness of our proposed approach in constructing annotated data and achieving noteworthy improvements in comparison to multiple baselines. Code, data and models are available at https://github.com/jjyunlp/GenerationRE.Downloads
Published
2024-03-24
How to Cite
Yu, J., Wang, X., & Chen, W. (2024). Reliable Data Generation and Selection for Low-Resource Relation Extraction. Proceedings of the AAAI Conference on Artificial Intelligence, 38(17), 19440-19448. https://doi.org/10.1609/aaai.v38i17.29915
Issue
Section
AAAI Technical Track on Natural Language Processing II