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A chatbot response generation system

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2020

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ACM

Zusammenfassung

Developing successful chatbots is a non-trivial endeavor. In particular, the creation of high-quality natural language responses for chatbots remains a challenging and time-consuming task that often depends on high-quality training data and deep domain knowledge. As a consequence, it is essential to engage experts in the chatbot response development process which have the required domain knowledge. However, current tool support to engage domain experts in the response generation process is limited and often does not go beyond the exchange of decoupled prototypes and spreadsheets. In this paper, we present a system that enables chatbot developers to efficiently engage domain experts in the chatbot response generation process. More specifically, we introduce the underlying architecture of a system that connects to existing chatbots via an API, provides two improvement mechanisms for domain experts to improve chatbot responses during their chatbot interaction, and helps chatbot developers to review the collected response improvements with a sentiment supported review dashboard. Overall, the design of the system and its improvement mechanisms are useful extensions for chatbot development systems in order to support chatbot developers and domain experts to collaboratively enhance the natural language responses of a chatbot.

Beschreibung

Feine, Jasper; Morana, Stefan; Maedche, Alexander (2020): A chatbot response generation system. Mensch und Computer 2020 - Tagungsband. DOI: 10.1145/3404983.3405508. New York: ACM. pp. 333–341. MCI: Full Paper. Magdeburg. 6.-9. September 2020

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