  Spoken Language Understanding (SLU) aims to extract the semantics frame of
user queries, which is a core component in a task-oriented dialog system. With
the burst of deep neural networks and the evolution of pre-trained language
models, the research of SLU has obtained significant breakthroughs. However,
there remains a lack of a comprehensive survey summarizing existing approaches
and recent trends, which motivated the work presented in this article. In this
paper, we survey recent advances and new frontiers in SLU. Specifically, we
give a thorough review of this research field, covering different aspects
including (1) new taxonomy: we provide a new perspective for SLU filed,
including single model vs. joint model, implicit joint modeling vs. explicit
joint modeling in joint model, non pre-trained paradigm vs. pre-trained
paradigm;(2) new frontiers: some emerging areas in complex SLU as well as the
corresponding challenges; (3) abundant open-source resources: to help the
community, we have collected, organized the related papers, baseline projects
and leaderboard on a public website where SLU researchers could directly access
to the recent progress. We hope that this survey can shed a light on future
research in SLU field.
