Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model

Abstract

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.

References in corpus (1)

• Sequence to Sequence Learning with Neural Networks

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• A Review of Dialogue Systems: From Trained Monkeys to Stochastic Parrots
• Exploring Teacher-Student Learning Approach for Multi-lingual Speech-to-Intent Classification