As ASP requires target task labels to train its private encoders, we compare TARS only with AFS and MoE, where TARS shows the best performance in MTL.
as shown in Figure 3, we observe that when TaRS covers sentences in CNLI and LCQMC, using its gating network that identifies that the unknown target tasks are the most similar to SNLI and QQP, respectively: highest weights are assigned to these two, but other source tasks also contribute, with non-zero weights.