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 data sets, evaluation metrics, experiment design and supply a summary of reported performance on the standard data sets.