A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction

Document-level relation extraction (DocRE) aims to extract relations among entities within a document, which is crucial for applications like knowledge graph construction. Existing methods usually assume that entities and their mentions are identified beforehand, which falls short of real-world applications. To overcome this limitation, we propose TAG, a novel tableto-graph generation model for joint extraction of entities and relations at document-level. To enhance the learning of task dependencies, TAG induces a latent graph among mentions, with different types of edges indicating different task information, which is further broadcast with a relational graph convolutional network. To alleviate the error propagation problem, we adapt the hierarchical agglomerative clustering algorithm to back-propagate task information at decoding stage. Experiments on the benchmark dataset, DocRED, demonstrate that TAG surpasses previous methods by a large margin and achieves state-of-the-art results 1 .