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、槽位填充这两个子任务.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.首先介绍了深度学习技术应用到口语理解的相关工作,然后从意图识别和槽位填充的关联关系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.
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 sound 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.
作者
魏鹏飞
曾碧
汪明慧
曾安
WEI Peng-Fei;ZENG Bi;WANG Ming-Hui;ZENG An(School of Computer Science and Technology,Guangdong University of Technology,Guangzhou 510006,China)
出处
《软件学报》
EI
CSCD
北大核心
2022年第11期4192-4216,共25页
Journal of Software
基金
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)。
关键词
意图识别
槽位填充
注意力机制
胶囊网络
任务对话系统
深度学习
intention recognition
slot filling
attention mechanism
capsule network
task dialogue system
deep learning
