  Typically, spoken language understanding (SLU) models are trained on
annotated data which are costly to gather. Aiming to reduce data needs for
bootstrapping a SLU system for a new language, we present a simple but
effective weight transfer approach using data from another language. The
approach is evaluated with our promising multi-task SLU framework developed
towards different languages. We evaluate our approach on the ATIS and a
real-world SLU dataset, showing that i) our monolingual models outperform the
state-of-the-art, ii) we can reduce data amounts needed for bootstrapping a SLU
system for a new language greatly, and iii) while multitask training improves
over separate training, different weight transfer settings may work best for
different SLU modules.
