Neural Coreference Resolution

Neural coreference resolution aims to automatically identify and group all mentions of the same real-world entity within a text, improving natural language understanding. Current research focuses on enhancing model performance by incorporating singleton mentions (entities mentioned only once), leveraging multi-task learning to integrate mention detection and coreference resolution, and utilizing richer contextual information such as discourse structure and rhetorical relations, often within neural network architectures like BERT and graph neural networks. These advancements are improving the accuracy and robustness of coreference resolution across diverse datasets and languages, with applications in areas such as business process automation and task-oriented dialogue systems.