Hard parameter sharing is the most commonly used approach to MTL in neural networks. It is generally applied by sharing the hidden layers between all tasks, while keeping several task-specific output layers. Hard parameter sharing greatly reduces the risk of overfitting. In fact, the risk of overfitting the shared parameters is an order N, where N is the number of tasks, smaller than overfitting the task-specific parameters, the output layers. Intuitively, this means the more tasks we are learning simultaneously, the more our model has to find a representation that captures all of the tasks and the less is our chance of overfitting on our original task.