基于深度学习的口语理解联合建模算法综述
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曾碧,zb9215@gdut.edu.cn

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国家自然科学基金(61772143);广东省自然科学基金(2018A030313868);广东省产学研重大专项(2016B010108004)


Survey of Joint Modeling Algorithms for Spoken Language Understanding Based on Deep Learning
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    摘要:

    口语理解是自然语言处理领域的研究热点之一,应用在个人助理、智能客服、人机对话、医疗等多个领域,口语理解技术指的是将机器接收到的用户输入的自然语言转换为语义表示,主要包含意图识别、槽位填充2个子任务.现阶段,使用深度学习对口语理解中意图识别和槽位填充任务的联合建模方法已成为主流并且获得了很好的效果,因此对基于深度学习的口语理解联合建模算法进行总结分析具有十分重要的意义.本文首先介绍了深度学习技术应用到口语理解相关工作,然后从意图识别和槽位填充的关联关系上对现有的研究工作进行剖析,并对不同模型的实验结果进行了对比分析和总结,最后给出了未来的研究方向及展望.

    Abstract:

    Spoken language understanding is one of the hot research topics in the field of natural language processing. It is applied in many fields such as personal assistants, intelligent customer service, human-computer dialogue, and medical treatment. Spoken language understanding technology refers to the conversion of natural language input by the user into semantics representation, which mainly includes 2 sub-tasks of intent recognition and slot filling. At this stage, the deep modeling of joint recognition methods for intent recognition and slot filling tasks in spoken language understanding has become mainstream and has achieved good results. Summarizing and analyzing the joint modeling algorithm of deep learning for spoken language learning is of great significance. First, it introduces the related work to the application of deep learning technology to spoken language understanding, and then the existing research work is analyzed from the relationship between intention recognition and slot filling. The experimental results of different models are compared and summarized. Finally, the challenges that future research may face are prospected.

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魏鹏飞,曾碧,汪明慧,曾安.基于深度学习的口语理解联合建模算法综述.软件学报,,():0

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  • 收稿日期:2020-08-04
  • 最后修改日期:2021-04-16
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  • 在线发布日期: 2021-08-02
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