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.
@article{arxiv.1907.02884,
title = {Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model},
author = {Giuseppe Castellucci and Valentina Bellomaria and Andrea Favalli and Raniero Romagnoli},
journal= {arXiv preprint arXiv:1907.02884},
year = {2019}
}