survey

A Survey of Joint Intent Detection and Slot Filling Models in Natural Language Understanding

Authors:

Soyeon Caren HanAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 8

Article No.: 156, Pages 1 - 38

https://doi.org/10.1145/3547138

Published: 23 December 2022 Publication History

Abstract

Intent classification, to identify the speaker’s intention, and slot filling, to label each token with a semantic type, are critical tasks in natural language understanding. Traditionally the two tasks have been addressed independently. More recently joint models that address the two tasks together have achieved state-of-the-art performance for each task and have shown there exists a strong relationship between the two. In this survey, we bring the coverage of methods up to 2021 including the many applications of deep learning in the field. As well as a technological survey, we look at issues addressed in the joint task and the approaches designed to address these issues. We cover datasets, evaluation metrics, and experiment design and supply a summary of reported performance on the standard datasets.

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Cited By

Wu YAlbornoz-De Luise RArevalillo-Herráez M(2025)On improving conversational interfaces in educational systemsComputer Speech & Language10.1016/j.csl.2024.10169389(101693)Online publication date: Jan-2025
https://doi.org/10.1016/j.csl.2024.101693
Muhammad YSalim NZainal A(2024)Joint intent detection and slot filling with syntactic and semantic features using multichannel CNN-BiLSTMPeerJ Computer Science10.7717/peerj-cs.234610(e2346)Online publication date: 8-Oct-2024
https://doi.org/10.7717/peerj-cs.2346
Hernández AOrtega-Mendoza RVillatoro-Tello ECamacho-Bello CPérez-Cortés O(2024)Natural Language Understanding for Navigation of Service Robots in Low-Resource Domains and Languages: Scenarios in Spanish and NahuatlMathematics10.3390/math1208113612:8(1136)Online publication date: 10-Apr-2024
https://doi.org/10.3390/math12081136
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Index Terms

A Survey of Joint Intent Detection and Slot Filling Models in Natural Language Understanding
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 8

August 2023

789 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3567473

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 23 December 2022

Online AM: 09 July 2022

Accepted: 20 June 2022

Revised: 06 June 2022

Received: 04 October 2021

Published in CSUR Volume 55, Issue 8

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Cited By

Wu YAlbornoz-De Luise RArevalillo-Herráez M(2025)On improving conversational interfaces in educational systemsComputer Speech & Language10.1016/j.csl.2024.10169389(101693)Online publication date: Jan-2025
https://doi.org/10.1016/j.csl.2024.101693
Muhammad YSalim NZainal A(2024)Joint intent detection and slot filling with syntactic and semantic features using multichannel CNN-BiLSTMPeerJ Computer Science10.7717/peerj-cs.234610(e2346)Online publication date: 8-Oct-2024
https://doi.org/10.7717/peerj-cs.2346
Hernández AOrtega-Mendoza RVillatoro-Tello ECamacho-Bello CPérez-Cortés O(2024)Natural Language Understanding for Navigation of Service Robots in Low-Resource Domains and Languages: Scenarios in Spanish and NahuatlMathematics10.3390/math1208113612:8(1136)Online publication date: 10-Apr-2024
https://doi.org/10.3390/math12081136
Sanguinetti MAtzori M(2024)Conversational Agents for Energy Awareness and Efficiency: A SurveyElectronics10.3390/electronics1302040113:2(401)Online publication date: 18-Jan-2024
https://doi.org/10.3390/electronics13020401
He ZLi SSong YCai Z(2024)Towards Building Condition-Based Cross-Modality Intention-Aware Human-AI Cooperation under VR EnvironmentProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642360(1-13)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642360
Chen GXu QZhan CWang FLiu KLiu HHao T(2024)Improving Open Intent Detection via Triplet-Contrastive Learning and Adaptive BoundaryIEEE Transactions on Consumer Electronics10.1109/TCE.2024.336389670:1(2806-2816)Online publication date: 19-Feb-2024
https://dl.acm.org/doi/10.1109/TCE.2024.3363896
Chen XLi LZhu YDeng STan CHuang FSi LZhang NChen H(2024)Sequence Labeling as Non-Autoregressive Dual-Query Set GenerationIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2024.335805332(1546-1558)Online publication date: 5-Feb-2024
https://dl.acm.org/doi/10.1109/TASLP.2024.3358053
Suresh VAït-Mokhtar SBrun CCalapodescu I(2024)An Adapter-Based Unified Model for Multiple Spoken Language Processing TasksICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448240(10676-10680)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448240
Pham TNguyen D(2024)JPIS: A Joint Model for Profile-Based Intent Detection and Slot Filling with Slot-to-Intent AttentionICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10446353(10446-10450)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10446353
Zhang BTu ZWang CSun HChu D(2024)Requirements elicitation and response generation for conversational servicesApplied Intelligence10.1007/s10489-024-05454-654:7(5576-5592)Online publication date: 23-Apr-2024
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