# A Survey on Spoken Language Understanding: Recent Advances and New Frontiers

arXiv: https://arxiv.org/abs/2103.03095 · HTML: https://mlsanity.com/paper/2103.03095/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 63 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 30 of 33)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [A Survey on Dialogue Systems: Recent Advances and New Frontiers](https://mlsanity.com/paper/1711.01731/) (2017)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [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)
- [End-to-End Slot Alignment and Recognition for Cross-Lingual NLU](https://mlsanity.com/paper/2004.14353/) (2020)
- [Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network](https://mlsanity.com/paper/2006.05702/) (2020)
- [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)
- [Discovering New Intents with Deep Aligned Clustering](https://mlsanity.com/paper/2012.08987/) (2020)
- [A Co-Interactive Transformer for Joint Slot Filling and Intent Detection](https://mlsanity.com/paper/2010.03880/) (2020)
- [Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement](https://mlsanity.com/paper/1911.08891/) (2019)
- [Deep Open Intent Classification with Adaptive Decision Boundary](https://mlsanity.com/paper/2012.10209/) (2020)
- [A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey](https://mlsanity.com/paper/2011.00564/) (2020)
- [Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems](https://mlsanity.com/paper/1911.09273/) (2019)
- [Towards Zero-Shot Frame Semantic Parsing for Domain Scaling](https://mlsanity.com/paper/1707.02363/) (2017)
- [Robust Zero-Shot Cross-Domain Slot Filling with Example Values](https://mlsanity.com/paper/1906.06870/) (2019)
- [Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling](https://mlsanity.com/paper/2004.11727/) (2020)
- [CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding](https://mlsanity.com/paper/1909.06937/) (2019)
- [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)
- [Few-shot Learning for Multi-label Intent Detection](https://mlsanity.com/paper/2010.05256/) (2020)
- [Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks](https://mlsanity.com/paper/1609.01462/) (2016)
- [OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding](https://mlsanity.com/paper/1801.05149/) (2018)
- [Sequential Dialogue Context Modeling for Spoken Language Understanding](https://mlsanity.com/paper/1705.03455/) (2017)
- [Dialogue State Induction Using Neural Latent Variable Models](https://mlsanity.com/paper/2008.05666/) (2020)

## Built on by (most cited first, 30 of 53)

- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [Textually Pretrained Speech Language Models](https://mlsanity.com/paper/2305.13009/) (2023)
- [Recent Advances in Speech Language Models: A Survey](https://mlsanity.com/paper/2410.03751/) (2024)
- [A Survey on Dialogue Summarization: Recent Advances and New Frontiers](https://mlsanity.com/paper/2107.03175/) (2021)
- [A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods](https://mlsanity.com/paper/2204.03508/) (2022)
- [GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.01925/) (2021)
- [A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding](https://mlsanity.com/paper/2304.04256/) (2023)
- [Prompt Perturbation Consistency Learning for Robust Language Models](https://mlsanity.com/paper/2402.15833/) (2024)
- [MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations](https://mlsanity.com/paper/2403.10943/) (2024)
- [Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding](https://mlsanity.com/paper/2305.13512/) (2023)
- [End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2311.09008/) (2023)
- [Learning Discriminative Representations and Decision Boundaries for Open Intent Detection](https://mlsanity.com/paper/2203.05823/) (2022)
- [GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding](https://mlsanity.com/paper/2204.08325/) (2022)
- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)
- [CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss](https://mlsanity.com/paper/2309.14580/) (2023)
- [Towards Relation Extraction From Speech](https://mlsanity.com/paper/2210.08759/) (2022)
- [UniDU: Towards A Unified Generative Dialogue Understanding Framework](https://mlsanity.com/paper/2204.04637/) (2022)
- [A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding](https://mlsanity.com/paper/2211.04023/) (2022)
- [A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery](https://mlsanity.com/paper/2304.07699/) (2023)
- [Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark](https://mlsanity.com/paper/2504.16427/) (2025)
- [Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding](https://mlsanity.com/paper/2112.11953/) (2021)
- [SDIF-DA: A Shallow-to-Deep Interaction Framework with Data Augmentation for Multi-modal Intent Detection](https://mlsanity.com/paper/2401.00424/) (2023)
- [ECLM: Entity Level Language Model for Spoken Language Understanding with Chain of Intent](https://mlsanity.com/paper/2403.04481/) (2024)
- [On-Device LLMs for Home Assistant: Dual Role in Intent Detection and Response Generation](https://mlsanity.com/paper/2502.12923/) (2025)
- [Smoothing Dialogue States for Open Conversational Machine Reading](https://mlsanity.com/paper/2108.12599/) (2021)
- [ASR-Generated Text for Language Model Pre-training Applied to Speech Tasks](https://mlsanity.com/paper/2207.01893/) (2022)
- [Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts](https://mlsanity.com/paper/2305.03237/) (2023)
- [Exploring Fine-Tuning of Large Audio Language Models for Spoken Language Understanding under Limited Speech Data](https://mlsanity.com/paper/2509.15389/) (2025)
- [A Result based Portable Framework for Spoken Language Understanding](https://mlsanity.com/paper/2103.06010/) (2021)
- [Sequence-Level Knowledge Distillation for Class-Incremental End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2305.13899/) (2023)

## Abstract (arXiv)

Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries, which is a core component in a task-oriented dialog system. With the burst of deep neural networks and the evolution of pre-trained language models, the research of SLU has obtained significant breakthroughs. However, there remains a lack of a comprehensive survey summarizing existing approaches and recent trends, which motivated the work presented in this article. In this paper, we survey recent advances and new frontiers in SLU. Specifically, we give a thorough review of this research field, covering different aspects including (1) new taxonomy: we provide a new perspective for SLU filed, including single model vs. joint model, implicit joint modeling vs. explicit joint modeling in joint model, non pre-trained paradigm vs. pre-trained paradigm;(2) new frontiers: some emerging areas in complex SLU as well as the corresponding challenges; (3) abundant open-source resources: to help the community, we have collected, organized the related papers, baseline projects and leaderboard on a public website where SLU researchers could directly access to the recent progress. We hope that this survey can shed a light on future research in SLU field.