  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.
