We approach the task of check-worthiness prediction as a multi-source learning problem, using different sources of annotation over the same training dataset.
this kind of neural network architecture for multi-task learning is known in the literature as hard parameter sharing (Caruana, 1993), and it can greatly reduce the risk of overfitting.