  In recent years, fostered by deep learning technologies and by the high
demand for conversational AI, various approaches have been proposed that
address the capacity to elicit and understand user's needs in task-oriented
dialogue systems. We focus on two core tasks, slot filling (SF) and intent
classification (IC), and survey how neural-based models have rapidly evolved to
address natural language understanding in dialogue systems. We introduce three
neural architectures: independent model, which model SF and IC separately,
joint models, which exploit the mutual benefit of the two tasks simultaneously,
and transfer learning models, that scale the model to new domains. We discuss
the current state of the research in SF and IC and highlight challenges that
still require attention.
