Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model
This work addresses multi-lingual natural language understanding for applications like virtual assistants, but it is incremental as it adapts existing BERT-based methods to a joint framework.
The paper tackled intent detection and slot filling in spoken language understanding by introducing Bert-Joint, a multi-lingual joint model, achieving strong performances on English benchmarks with few annotated data and similar results on a new Italian dataset.
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.