Abstract
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.
Similar content being viewed by others
Data Availability
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 (wuyirui@hhu.edu.cn).
References
Qi L, Wang X, Xu X, Dou W, Li S (2020) Privacy-aware cross-platform service recommendation based on enhanced locality-sensitive hashing. IEEE Trans Netw Sci Eng:1–1
Xu X, Shen B, Yin X, Khosravi M R, Wu H, Qi L, Wan S (2020) Edge server quantification and placement for offloading social media services in industrial cognitive iov. IEEE Trans Ind Inf 99:11. https://doi.org/10.1109/TII.2020.2987994
Qi L, He Q, Chen F, Zhang X, Dou W, Ni Q (2020) Data-driven web apis recommendation for building web applications. IEEE Trans Big Data:1–1
Qi L, He Q, Chen F, Dou W, Wan S, Zhang X, Xu X (2019) Finding all you need: Web apis recommendation in web of things through keywords search. IEEE Trans Comput Soc Syst 6 (5):1063–1072
Xu X, Liu X, Xu Z, Dai F, Zhang X, Qi L (2020) Trust-oriented iot service placement for smart cities in edge computing. IEEE Internet Things J 7(5):4084–4091
Weizenbaum J (1966) Eliza-a computer program for the study of natural language communication between man and machine. Commun ACM 9(1):36–45
Simmons R F (1967) Answering english questions by computer. Autom Lang Process 8(1):253
Xu X, Mo R, Dai F, Lin W, Wan S, Dou W (2020) Dynamic resource provisioning with fault tolerance for data-intensive meteorological workflows in cloud. IEEE Trans Ind Inf 16(9):6172–6181
Xu X, Wu Q, Qi L, Dou W, Tsai S-B, Bhuiyan Z A (2020) Trust-aware service offloading for video surveillance in edge computing enabled internet of vehicles. IEEE Trans Intell Transp Syst:1–10. https://doi.org/10.1109/TITS.2020.2995622
Xu X, Zhang X, Liu X, Jiang J, Qi L, Bhuiyan M Z A (2020) Adaptive computation offloading with edge for 5g-envisioned internet of connected vehicles. IEEE Trans Intell Transp Syst:1–10. https://doi.org/10.1109/TITS.2020.2982186
Ravuri S V, Stolcke A (2015) Recurrent neural network and LSTM models for lexical utterance classification. In: Proceedings of 16th Annual Conference of the International Speech Communication Association, pp 135–139
Xia C, Zhang C, Yan X, Chang Y, Yu P S (2018) Zero-shot user intent detection via capsule neural networks. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 3090–3099
Singh R R, Miller T, Newn J, Velloso E, Vetere F, Sonenberg L (2020) Combining gaze and AI planning for online human intention recognition. Artif. Intell. 284:103275
Chiu J P C, Nichols E (2016) Named entity recognition with bidirectional lstm-cnns. Trans Assoc Comput Linguist 4:357– 370
Ma X, Hovy E H (2016) End-to-end sequence labeling via bi-directional lstm-cnns-crf. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics
Liu T, Yao J-G, Lin C-Y (2019) Towards improving neural named entity recognition with gazetteers. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, pp 5301–5307
Liu Z, Winata G I, Xu P, Fung P (2020) Coach: A coarse-to-fine approach for cross-domain slot filling. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 19–25
Zhu S, Zhao Z, Ma R, Yu K (2020) Prior knowledge driven label embedding for slot filling in natural language understanding. IEEE ACM Trans Audio Speech Lang Process 28:1440– 1451
Zhang X, Wang H (2016) A joint model of intent determination and slot filling for spoken language understanding.. In: IJCAI, vol 16, pp 2993–2999
Liu B, Lane I (2016) Attention-based recurrent neural network models for joint intent detection and slot filling. In: Proceedings of 17th Annual Conference of the International Speech Communication Association, pp 685–689
Chen S, Yu S (2019) WAIS: word attention for joint intent detection and slot filling. In: Proceedings of The Thirty-Third AAAI Conference on Artificial Intelligence, pp 9927–9928
Yin W, Kann K, Yu M, Schütze H (2017) Comparative study of cnn and rnn for natural language processing. arXiv:1702.01923
Xu H, Saenko K (2016) Ask, attend and answer: Exploring question-guided spatial attention for visual question answering. In: Proceedings of European Conference on Computer Vision , vol 9911, pp 451–466
Yang Z, He X, Gao J, Deng L, Smola A J (2016) Stacked attention networks for image question answering. In: Proceedings of Computer Vision and Pattern Recognition, pp 21–29
Anderson P, He X, Buehler C, Teney D, Johnson M, Gould S, Zhang L (2018) Bottom-up and top-down attention for image captioning and visual question answering. In: Proceedings of Computer Vision and Pattern Recognition, pp 6077– 6086
Woo S, Park J, Lee J-Y, So Kweon I (2018) Cbam: Convolutional block attention module. In: Proceedings of European Conference on Computer Vision, pp 3–19
Cao Y, Xu J, Lin S, Wei F, Hu H (2019) Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of International Conference on Computer Vision Workshops, pp 1971–1980
Zhao X, Sang L, Ding G, Han J, Di N, Yan C (2019) Recurrent attention model for pedestrian attribute recognition. In: Proceedings of AAAI Conference on Artificial Intelligence, pp 9275–9282
Zhao T, Wu X (2019) Pyramid feature attention network for saliency detection. In: Proceedings of Computer Vision and Pattern Recognition, pp 3085–3094
Liu S, Huang D, Wang Y (2018) Receptive field block net for accurate and fast object detection. In: Proceedings of European Conference on Computer Vision, vol 11215, pp 404–419
Li X, Wang W, Hu X, Yang J (2019) Selective kernel networks. In: Proceedings of Computer Vision and Pattern Recognition, pp 510–519
Xiao Y, Xue M, Lu T, Wu Y, Palaiahnakote S (2019) A text-context-aware CNN network for multi-oriented and multi-language scene text detection. In: Proceedings of International Conference on Document Analysis and Recognition, pp 695– 700
Hakkani-Tür D, Tür G, Celikyilmaz A, Chen Y-N, Gao J, Deng L, Wang Y-Y (2016) Multi-domain joint semantic frame parsing using bi-directional rnn-lstm.. In: Proceedings of Annual Conference of the International Speech Communication Association, pp 715–719
Liu B, Lane I (2016) Attention-based recurrent neural network models for joint intent detection and slot filling. arXiv:1609.01454
Goo C-W, Gao G, Hsu Y-K, Huo C-L, Chen T-C, Hsu K-W, Chen Y-N (2018) Slot-gated modeling for joint slot filling and intent prediction. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp 753–757
Li C, Li L, Qi J (2018) A self-attentive model with gate mechanism for spoken language understanding. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 3824–3833
Wang Y, Shen Y, Jin H (2018) A bi-model based rnn semantic frame parsing model for intent detection and slot filling. arXiv:1812.10235
Haihong E, Niu P, Chen Z, Song M (2019) A novel bi-directional interrelated model for joint intent detection and slot filling. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 5467–5471
Funding
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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wu, Y., Mao, W. & Feng, J. AI for Online Customer Service: Intent Recognition and Slot Filling Based on Deep Learning Technology. Mobile Netw Appl 27, 2305–2317 (2022). https://doi.org/10.1007/s11036-021-01795-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-021-01795-5