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.

中文翻译：

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口语理解调查：最新进展和新领域

口语理解（SLU）旨在提取用户查询的语义框架，这是面向任务的对话框系统中的核心组件。随着深度神经网络的爆发和预训练语言模型的发展，SLU的研究取得了重大突破。但是，仍然缺乏对现有方法和最新趋势进行总结的全面调查，这激发了本文中介绍的工作。在本文中，我们调查了SLU的最新进展和新领域。具体来说，我们对这个研究领域进行了全面的回顾，涵盖了不同方面，其中包括（1）新的分类法：我们为SLU领域提供了新的视角，包括联合模型中的单模型与联合模型，隐式联合建模与显式联合建模。 ，非预训练范式与预训练范式；（2）新领域：复杂SLU中的一些新兴领域以及相应的挑战；（3）丰富的开源资源：为了帮助社区，我们在一个公共网站上收集，整理了相关论文，基准项目和排行榜，SLU研究人员可以直接从中获取最新进展。我们希望这项调查能够为SLU领域的未来研究提供启发。