The supervised component induces label information (e.g., relevant vs. non-relevant) directly in the network through the classification loss L C (Λ, Φ), which is computed on the labeled instances in the source event, D l S .
this branch of the network, as shown at the top in Figure 1, takes the shared representations z as input and pass it through a task-specific dense layer where V c is the corresponding weight matrix.