Hard parameter sharing is a multi-task learning (MTL) approach for neural networks in which the hidden layers are shared across all tasks, while each task keeps its own task-specific output layers. Because the shared layers must fit patterns common to every task rather than the idiosyncrasies of any single task, this approach reduces the risk of overfitting compared to training a separate network per task.