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.
翻译:语言语言理解(SLU)旨在提取用户查询的语义框架,这是任务导向对话系统的核心组成部分。随着深层神经网络的破碎和预先培训的语言模式的演变,SLU的研究取得了重大突破。然而,目前仍然缺乏一项全面调查,总结了推动本篇文章所述工作的现有做法和最新趋势。在本文件中,我们调查了SLU的最新进展和新疆界。具体地说,我们对这一研究领域进行了彻底审查,包括:(1) 新的分类:我们为SLU提交的研究领域提供了一个新视角,包括单一模型与联合模型、隐含的联合模型与明确的联合模型,在联合模型中,未事先培训的模式与事先培训的模式;(2) 新疆域:复杂的SLU的一些新兴领域以及相应的挑战;(3) 丰富的开放源资源:为了帮助社区,我们收集、组织相关文件、基线项目和领导板块,放在SLU研究人员可直接获取近期进展的公共网站上。我们希望这次调查能够让SLU的未来研究领域有一个光。