  With the recent explosion in popularity of voice assistant devices, there is
a growing interest in making them available to user populations in additional
countries and languages. However, to provide the highest accuracy and best
performance for specific user populations, most existing voice assistant models
are developed individually for each region or language, which requires linear
investment of effort. In this paper, we propose a general multilingual model
framework for Natural Language Understanding (NLU) models, which can help
bootstrap new language models faster and reduce the amount of effort required
to develop each language separately. We explore how different deep learning
architectures affect multilingual NLU model performance. Our experimental
results show that these multilingual models can reach same or better
performance compared to monolingual models across language-specific test data
while require less effort in creating features and model maintenance.
