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A Contextualized Government Service Chatbot for Individuals with limited Information Literacy

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Wisdom, Well-Being, Win-Win (iConference 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14596))

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Abstract

Improving the Q&A ability of government service chatbots (GSCs) has become an important issue. In practice, a large number of users with poor information literacy often pose vague questions, which makes it challenging for GSCs to comprehend their inquiries within a specific context. In order to enhance contextualization, this study has constructed a multi-turn dialogue model that incorporates R-GCN and fuzzy logic to base on the “question-answer-context” matching process. To obtain more accurate context, we propose a re-question mechanism to further press for contextual details. Additionally, we introduce the sub-graph matching mechanism of fuzzy logic and R-GCN to improve the accuracy of implicitly representation of Chinese logic in the contextualized matching process. This mechanism allows us to prune the context-irrelevant parts in the “answer” and obtain more complete context information. We collected over 300,000 words of real cases as the test-set. The results of the experiments show that this model can significantly improve the contextualized reasoning ability of GSCs in a more humanized way. The innovative response generation method in this research, which utilizes “question-answer-context” matching, is more suitable for complex scenarios where the user may not be articulate. It helps to lower the barrier for accessing government services and provides more user-friendly assistance to individuals with limited information literacy.

Z. Lian and H. Meiyin—Contributed equally to this work, should be considered co-first authors.

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Notes

  1. 1.

    In fuzzy logic, a membership function is employed for characterizing the extent to which an element belongs to a specific fuzzy set. It assigns a membership degree value, ranging from 0 to 1, to the element, facilitating the management of imprecise information and the execution of fuzzy reasoning.

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Acknowledgments

The authors gratefully acknowledge the financial support provided by the National Social Science Foundation of China (20ZDA039), the National Science Foundation of China ("Multi-dimensional Analysis of Policy Driven by Big Data", 72293571), Ministry of Education in China (MOE) Project of Humanities and Social Sciences (Project No. 23YJC630107).

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Authors and Affiliations

  1. Nankai University, Tian**, 300071, China

    Zhixuan Lian, Meiyin Huang & Fang Wang

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  1. Zhixuan Lian
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  2. Meiyin Huang
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  3. Fang Wang
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Corresponding author

Correspondence to Zhixuan Lian .

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Editors and Affiliations

  1. iSchool organization, Berlin, Germany

    Isaac Sserwanga

  2. University of Tsukuba, Tsukuba, Japan

    Hideo Joho

  3. Jilin University, Changchun, China

    Jie Ma

  4. Stockholm University, Stockholm, Sweden

    Preben Hansen

  5. Wuhan University, Wuhan, China

    Dan Wu

  6. University of Tsukuba, Tsukuba, Japan

    Masanori Koizumi

  7. University of California, Los Angeles, CA, USA

    Anne J. Gilliland

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Lian, Z., Huang, M., Wang, F. (2024). A Contextualized Government Service Chatbot for Individuals with limited Information Literacy. In: Sserwanga, I., et al. Wisdom, Well-Being, Win-Win. iConference 2024. Lecture Notes in Computer Science, vol 14596. Springer, Cham. https://doi.org/10.1007/978-3-031-57850-2_15

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  • DOI: https://doi.org/10.1007/978-3-031-57850-2_15

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  • Online ISBN: 978-3-031-57850-2

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