Natural language understanding approaches based on joint task of intent detection and slot filling for IoT voice interac
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S.I. : APPLYING ARTIFICIAL INTELLIGENCE TO THE INTERNET OF THINGS
Natural language understanding approaches based on joint task of intent detection and slot filling for IoT voice interaction Pin Ni1 • Yuming Li1 • Gangmin Li2 • Victor Chang3 Received: 8 January 2020 / Accepted: 19 February 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Internet of Things (IoT) based voice interaction system, as a new artificial intelligence application, provides a new human– computer interaction mode. The more intelligent and efficient communication approach poses greater challenges to the semantic understanding module in the system. Facing with the complex and diverse interactive scenarios in practical applications, the academia and the industry urgently need more powerful Natural Language Understanding (NLU) methods as support. Intent Detection and Slot Filling joint task, as one of the core sub-tasks in NLU, has been widely used in different human–computer interaction scenarios. In the current era of deep learning, the joint task of Intent Detection and Slot Filling has also changed from previous rule-based methods to deep learning-based methods. It is an important problem to explore how to realize the models of these tasks to be refined and targeted designed, and to make the Intent Detection task better serve the improvement of precision of Slot Filling task by connecting the before and after tasks. It has great significance for building a more humanized IoT voice interaction system. In this study, we designed two joint models to realize Intent Detection and Slot Filling joint task. For the Intent Detection type task, one is based on BiGRU-AttCapsuleNet (hybrid-based model) and the other is based on the RCNN model. Both methods use the BiGRU-CRF model for the Slot Filling type task. The hybrid-based model can enhance the semantic capture capability of a single model. And by combining specialized models built independently for each task to achieve a complete joint task, it can be better to achieve optimal performance on each task. This study also carried out detailed comparative experiments of tasks and joint tasks on multiple datasets. Experiments show that the joint models have achieved competitive results in 7 typical datasets included in multiple scenarios in English and Chinese compared with other models. Keywords Internet of Things Artificial intelligence Natural language understanding Voice interaction Intent detection and slot filling Capsule network
1 Introduction & Victor Chang [email protected] Pin Ni [email protected] Yuming Li [email protected] Gangmin Li [email protected] 1
Department of Computer Science, University of Liverpool, Liverpool, UK
2
Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China
3
School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK
The way of voice interaction has become a new idea for the co
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