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

arXiv: https://arxiv.org/abs/1907.02884 · HTML: https://mlsanity.com/paper/1907.02884/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2019.

## Built on by (most cited first, 24 of 24)

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- [Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss](https://mlsanity.com/paper/2203.09278/) (2022)
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- [A survey of joint intent detection and slot-filling models in natural language understanding](https://mlsanity.com/paper/2101.08091/) (2021)
- [Exploring Teacher-Student Learning Approach for Multi-lingual Speech-to-Intent Classification](https://mlsanity.com/paper/2109.13486/) (2021)
- [Controllable Response Generation for Assistive Use-cases](https://mlsanity.com/paper/2112.02246/) (2021)
- [Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction](https://mlsanity.com/paper/2212.10728/) (2022)
- [Reward-Driven Interaction: Enhancing Proactive Dialogue Agents through User Satisfaction Prediction](https://mlsanity.com/paper/2505.18731/) (2025)
- [Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning](https://mlsanity.com/paper/2506.03035/) (2025)
- [Future of AI Models: A Computational perspective on Model collapse](https://mlsanity.com/paper/2511.05535/) (2025)
- [ESAinsTOD: A Unified End-to-End Schema-Aware Instruction-Tuning Framework for Task-Oriented Dialog Modeling](https://mlsanity.com/paper/2603.09691/) (2026)

## Abstract (arXiv)

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.