LAI: Label Annotation Interaction based Representation Enhancement for End to End Relation Extraction

End-to-end relation extraction (E2ERE) generally performs named entity recognition and relation extraction either simultaneously or sequentially. While numerous studies on E2ERE have centered on enhancing span representations to improve model performance, challenges remain due to the gaps between subtasks (named entity recognition and relation extraction) and the modeling discrepancies between entities and relations. In this paper, we propose a novel Label Annotation Interaction based representation enhancement method for E2ERE, which institutes a two-phase semantic interaction to augment representations. Specifically, we firstly feed label annotations that are easy to manually annotate into a language model, and conduct the first round interaction between three types of tokens with a partial attention mechanism; Then construct a latent multi-view graph to capture various possible links between label and entity (pair) nodes, facilitating the second round interaction between entities and labels. A series of comparative experiments with methods of various transformer-based architectures currently in use have shown that LAI-Net can maintain performance on par with the current SOTA in terms of NER task, and achieves significant improvements over existing SOTA models in terms of RE task.