A typical approach is to pre-train the model on labeled data from a richly resourced language, and then either apply it directly on a target language (Upadhyay et al., 2018) or finetune it on a smaller amount of supervised data from a target language (Do and Gaspers, 2019).
both in SLU and other NLP tasks, prior work on CLTL typically utilized all available source data to transfer knowledge, as it has been mainly inves-tigated in rather small Academic settings.