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.

本文介绍了 Bert-Joint，一种利用多语言联合文本分类和序列标注框架的模型，旨在利用 “无递归” 模型成功应用于口语自然语言理解的任务上，并通过实验验证了该模型在英语基准数据集上表现出了强大的性能，处理少量的标注数据时表现良好。此外，我们还为意大利语语言标注了一个新数据集，并观察到了类似的性能，而不需要更改模型。