# A survey of joint intent detection and slot-filling models in natural language understanding

arXiv: https://arxiv.org/abs/2101.08091 · HTML: https://mlsanity.com/paper/2101.08091/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 47 of its 174 references that are in the map.
- First posted on arXiv 20 Jan 2021.

## Builds on (most cited first, 30 of 47)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://mlsanity.com/paper/1904.01608/) (2019)
- [Survey on Evaluation Methods for Dialogue Systems](https://mlsanity.com/paper/1905.04071/) (2019)
- [Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog](https://mlsanity.com/paper/1810.13327/) (2018)
- [A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding](https://mlsanity.com/paper/1909.02188/) (2019)
- [A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1907.00390/) (2019)
- [CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP](https://mlsanity.com/paper/2006.06402/) (2020)
- [Joint Slot Filling and Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1812.09471/) (2018)
- [Deep Unknown Intent Detection with Margin Loss](https://mlsanity.com/paper/1906.00434/) (2019)
- [Zero-shot User Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1809.00385/) (2018)
- [A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding](https://mlsanity.com/paper/1909.06937/) (2019)
- [A Survey of Available Corpora for Building Data-Driven Dialogue Systems](https://mlsanity.com/paper/1512.05742/) (2015)
- [Learning to Classify Intents and Slot Labels Given a Handful of Examples](https://mlsanity.com/paper/2004.10793/) (2020)
- [Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling](https://mlsanity.com/paper/1601.01530/) (2016)
- [Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model](https://mlsanity.com/paper/1907.02884/) (2019)
- [Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables](https://mlsanity.com/paper/1911.04081/) (2019)
- [Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks](https://mlsanity.com/paper/1609.01462/) (2016)
- [Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates](https://mlsanity.com/paper/1807.00072/) (2018)
- [OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding](https://mlsanity.com/paper/1801.05149/) (2018)
- [Almawave-SLU: A new dataset for SLU in Italian](https://mlsanity.com/paper/1907.07526/) (2019)
- [Multi-Task Networks With Universe, Group, and Task Feature Learning](https://mlsanity.com/paper/1907.01791/) (2019)
- [CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots](https://mlsanity.com/paper/1909.08705/) (2019)
- [Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken Language Understanding](https://mlsanity.com/paper/1608.02097/) (2016)
- [Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents](https://mlsanity.com/paper/1811.05370/) (2018)
- [Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances](https://mlsanity.com/paper/1904.10500/) (2019)
- [Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant](https://mlsanity.com/paper/1803.11326/) (2018)

## Abstract (arXiv)

Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks have been deemed to proceed independently. However, more recently, joint models for intent classification and slot filling have achieved state-of-the-art performance, and have proved that there exists a strong relationship between the two tasks. This article is a compilation of past work in natural language understanding, especially joint intent classification and slot filling. We observe three milestones in this research so far: Intent detection to identify the speaker's intention, slot filling to label each word token in the speech/text, and finally, joint intent classification and slot filling tasks. In this article, we describe trends, approaches, issues, data sets, evaluation metrics in intent classification and slot filling. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. To interpret the state-of-the-art trends, we provide multiple tables that describe and summarise past research along different dimensions, including the types of features, base approaches, and dataset domain used.