  Intent Detection and Slot Filling are two pillar tasks in Spoken Natural
Language Understanding. Common approaches adopt joint Deep Learning
architectures in attention-based recurrent frameworks. In this work, we aim at
exploiting the success of "recurrence-less" models for these tasks. We
introduce Bert-Joint, i.e., a multi-lingual joint text classification and
sequence labeling framework. The experimental evaluation over two well-known
English benchmarks demonstrates the strong performances that can be obtained
with this model, even when few annotated data is available. Moreover, we
annotated a new dataset for the Italian language, and we observed similar
performances without the need for changing the model.
