However, it suffers from several drawbacks: First, in order to maintain the SLU performance on both original and new expressions, it usually requires almost retraining the model using the whole dataset.
as the size of training set grows, it becomes infeasible to repeatedly conduct time consuming retraining on such a large dataset.