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A Discourse Structural Analysis Survey and Taxonomy for Chatbots

Vanguard et al.

AIS Transactions on Human-Computer Interaction2025https://doi.org/10.17705/1thci.00217article
AJG 2ABDC A
Weight
0.37

Abstract

As organizations increasingly adopt chatbots, the complexity of chatbot–human conversations also increases. The success of chatbots depends on their ability to interpret discourse contexts and provide meaningful responses that resonate with users. Discourse-analysis methods, which can elucidate the intricacies of conversation design, discourse structures, and semantics, play an instrumental role in guiding efforts to design human–chatbot conversations and ensuring that they resemble human-human conversations. For this research, we meticulously reviewed 92 scholarly papers that considered discourse-analysis methods for chatbot development to unravel the connections between discourse structures and the dimensions of chatbot design. Furthermore, we examine the extent to which the chatbot dimensions across the four distinct chatbot conversation lifecycle phases align with one another to ensure a comprehensive, cradle-to-grave approach. Additionally, we delved into the conversation modeling dilemmas that emerge when designing contextually sensitive chatbots.

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https://doi.org/https://doi.org/10.17705/1thci.00217

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@article{vanguard2025,
  title        = {{A Discourse Structural Analysis Survey and Taxonomy for Chatbots}},
  author       = {Vanguard et al.},
  journal      = {AIS Transactions on Human-Computer Interaction},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.17705/1thci.00217},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.