VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction
This addresses the long-tail distribution issue in document-level relation extraction, which is an incremental improvement for natural language processing tasks.
The paper tackles the problem of imbalanced datasets in document-level relation extraction by proposing a data augmentation framework using generative models, which outperforms state-of-the-art models on benchmark datasets.
Document-level Relation Extraction (DocRE) aims to identify relationships between entity pairs within a document. However, most existing methods assume a uniform label distribution, resulting in suboptimal performance on real-world, imbalanced datasets. To tackle this challenge, we propose a novel data augmentation approach using generative models to enhance data from the embedding space. Our method leverages the Variational Autoencoder (VAE) architecture to capture all relation-wise distributions formed by entity pair representations and augment data for underrepresented relations. To better capture the multi-label nature of DocRE, we parameterize the VAE's latent space with a Diffusion Model. Additionally, we introduce a hierarchical training framework to integrate the proposed VAE-based augmentation module into DocRE systems. Experiments on two benchmark datasets demonstrate that our method outperforms state-of-the-art models, effectively addressing the long-tail distribution problem in DocRE.