ChatGPT引发了新一轮的科技革命,使得对话系统成为研究热点.口语理解(Spoken Language Under-standing,SLU)作为任务型对话系统的第一部分,对系统整体的表现具有重要影响.在最近几年中,得益于大规模语言模型的成功,口语理解任务取得了较大的发展.然而,现有工作大多基于书面语数据集完成,无法很好地应对真实口语场景.为此,该文面向与书面语相对的口语,重点关注医疗领域这一应用场景,对现有的医疗领域对话系统口语理解任务进行综述.具体地,该文阐述了医疗口语理解任务的难点与挑战,并从数据集、算法和应用的层面梳理了医疗口语理解的研究现状及不足之处.最后,该文结合生成式大模型的最新进展,给出了医疗口语理解问题新的研究方向.
A Survey of Spoken Language Understanding in Medical Field
As the firststep in task-oriented dialogue system(TOD),Spoken Language Understanding(SLU)gov-erns the overall system performance.The past few years have witnessed a great progress of SLU due to the huge success of Large Language Model(LLM).This paper investigated the SLU task(in contrast to written language un-derstanding)with a focus on medical field.Specifically,this paper illustrates the difficulties and challenges in medi-cal SLU task.And it summarizes the progress and shortcomings of the existing researches from the perspectives of datasets,algorithms and applications.Besides,combined with the latest progress of generative LLM,this paper outlines the new research direction in this field.
task-oriented dialogue systemspoken language understandingmedical fieldgenerative large language model
NETL
NSTL
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