Learned Task Relationships in Multi-task Models
Abstract
We propose a framework that learns cross-task relationships in multi-task models by estimating the joint distribution of task labels. This approach improves performance via transfer learning and enhances information extraction. Although our framework applies to any multi-task system, we demonstrate its efficacy within YouTube’s production recommendation stacks. Experiments across the Notifications, Homepage, and Watch Next surfaces show improvements in both accuracy and satisfied user engagement. Finally, we propose a workflow template to facilitate broader future implementation.
Research Areas
