Task Decomposition for Efficient Annotation

The article introduces a method for task decomposition in structured annotation to enhance efficiency and reduce the inferential load on annotators. It presents a formal model based on centering theory to identify salient anchor entities, allowing for the effective breakdown of complex annotation tasks into manageable sub-tasks. This approach not only improves cost-efficiency but also optimizes the allocation of sub-tasks among heterogeneous annotators, which is crucial for practitioners aiming to streamline annotation processes in large-scale AI projects.