  Recently, multi-task spoken language understanding (SLU) models have emerged,
designed to address various speech processing tasks. However, these models
often rely on a large number of parameters. Also, they often encounter
difficulties in adapting to new data for a specific task without experiencing
catastrophic forgetting of previously trained tasks. In this study, we propose
finding task-specific subnetworks within a multi-task SLU model via neural
network pruning. In addition to model compression, we expect that the
forgetting of previously trained tasks can be mitigated by updating only a
task-specific subnetwork. We conduct experiments on top of the state-of-the-art
multi-task SLU model ``UniverSLU'', trained for several tasks such as emotion
recognition (ER), intent classification (IC), and automatic speech recognition
(ASR). We show that pruned models were successful in adapting to additional ASR
or IC data with minimal performance degradation on previously trained tasks.
