A stochastically-based method for natural language understanding has been ported from the American ATIS (Air Travel Information Services) to the French MASK (Multimodal-Multimedia Automated Service Kiosk) task. The porting was carried out by designing and annotating a corpus of semantic representations via a semi-automatic iterative labeling. The study shows that domain and language porting is rather flexible, since it is sufficient to train the system on data sets specific to the application and language. A limiting factor of the current implementation is the quality of the semantic representation and the use of query preprocessing strategies which strongly suffer from human influence. The performances of the stochastically-based and a rule-based method are compared on both tasks.