Joint modeling method of question intent detection and slot filling for domain-oriented question answering system
Data Technologies and Applications
ISSN: 2514-9288
Article publication date: 10 February 2023
Issue publication date: 15 November 2023
Abstract
Purpose
Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.
Design/methodology/approach
This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.
Findings
The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.
Originality/value
This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.
Keywords
Acknowledgements
Funding: The authors are very grateful to the editors and reviewers for their valuable comments and suggestions. The research was supported by Hebei Natural Science Foundation (grant numbers F2022208002), Science and Technology Project of Hebei Education Department (Key program) (grant numbers ZD2021048); and Hebei Province Special Research and Development Plan Project (grant numbers SJMYF2022X13).
Ethical Approval and consent to participate: All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all the participants.
Consent for publication: Written informed consent for publication was obtained from all participants.
Human and animal ethics: Not Applicable.
Availability of supporting data: The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.
Competing interests: The authors have no conflicts of interest to declare relevant to the content of this article.
Citation
Wang, H., Yang, D., Guo, L. and Zhang, X. (2023), "Joint modeling method of question intent detection and slot filling for domain-oriented question answering system", Data Technologies and Applications, Vol. 57 No. 5, pp. 696-718. https://doi.org/10.1108/DTA-07-2022-0281
Publisher
:Emerald Publishing Limited
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