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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
