Nowcasting In Chatbot Design
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Nowcasting in Chatbot Design
The rise of intelligent conversation agents, or chatbots, are responsible for the dramatic decrease in remote customer service agent jobs. However, chatbots in their current form, are far from infallible. We theorize that there is an inherent trade-off between a chatbot's response relevance and conversational efficiency in the standard knowledge-bank architecture. Knowledge bank size increases the relevance of successfully queried results, but also increases the difficulty of disambiguating user intents. This inherent trade-off leads to the development of unintelligent fail-safe artifacts such as user confirmations. We argue that, in order to improve user experience and satisfaction, we must decouple knowledge bank size from conversational efficiency. To achieve this, we first design a new artifact that we dub the sequential FAQ (sFAQ) and then evaluate its causal impact on user satisfaction. An sFAQ uses machine learning techniques to first discover common user service journey patterns, then leverage these learned patterns to predict likely subsequent inputs given any focal sequence of inputs. We show that by proactively suggesting potential questions to the user, we can reduce the need for natural language input and thus reduce the need to disambiguate user intent. We then use a novel application of regression discontinuity design (RDD) to study the causal impact of the eliminated reconfirmation dialogues on user satisfaction. Combined, we are able to demonstrate that by eliminating the unintelligent fail-safe artifacts such as user confirmations, the sFAQ will increase satisfaction. Our approach of combining predictive machine learning and causal econometric analysis enables us to open the black box for the underlying causal mechanism linking sFAQ and user satisfaction. This kind of mechanism identification would not be possible even with experimental testing in the field. Our methods and results have useful implications for chatbot applications and user interface design science.
Chatbot Research and Design
This book constitutes the proceedings of the 5th International Workshop on Chatbot Research and Design, CONVERSATIONS 2021, which was held during November 2021.Due to COVID-19 pandemic the conference was held online. The 12 papers included in this volume were carefully reviewed and selected from a total of 25 submissions. The papers in the proceedings are structured in four topical groups: Chatbot User Insight, Chatbots Supporting Collaboration and Social Interaction, and Chatbot UX and Design.
Chatbot Research and Design
This book constitutes the refereed proceedings of the Third International Workshop on Chatbot Research and Design, CONVERSATIONS 2019, held in Amsterdam, The Netherlands, in November 2019. The 18 revised full papers presented in this volume were carefully reviewed and selected from 31 submissions. The papers are grouped in the following topical sections: user and communication studies user experience and design, chatbots for collaboration, chatbots for customer service, and chatbots in education.