Cloud/edge computing and deep learning greatly improve performance of semantic understanding systems, where cloud/edge computing provides flexible, pervasive computation and storage capabilities to support variant applications, and deep learning models could comprehend text inputs by consuming computing and storage resource. Therefore, we propose to implement an intelligent online custom service system with power of both technologies. Essentially, task of semantic understanding consists of two subtasks, i.e., intent recognition and slot filling. To prevent error accumulation caused by modeling two subtasks independently, we propose to jointly model both subtasks in an end-to-end neural network. Specifically, the proposed method firstly extracts distinctive features with a dual structure to take full advantage of interactive and level information between two sub-tasks. Afterwards, we introduce attention scheme to enhance feature representation by involving sentence-level context information. With the support of cloud/edge computing infrastructure, we deploy the proposed network to work as an intelligent dialogue system for electrical customer service. During experiments, we test the proposed method and several comparative studies on public ATIS and our collected PSCF dataset. Experiment results prove the effectiveness of the proposed method by obtaining accurate and promising results.
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The data used to support the findings of this study were supplied by Wenqin Mao under license and so cannot be made freely available. Requests for access to these data should be made to Yirui Wu (firstname.lastname@example.org).
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This work was supported by National Key R&D Program of China under Grant 2018YFC0407901, the Fundamental Research Funds for the Central Universities under Grant B200202177, the Natural Science Foundation of China under Grant 61702160, the Natural Science Foundation of Jiangsu Province under Grant BK20170892.
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Wu, Y., Mao, W. & Feng, J. AI for Online Customer Service: Intent Recognition and Slot Filling Based on Deep Learning Technology. Mobile Netw Appl (2021). https://doi.org/10.1007/s11036-021-01795-5
- Cloud/edge computing for deep learning
- Combing AI and cloud/edge for custom service
- Semantic understanding
- Intent recognition
- Slot filling