What Makes a Good Conversation? Challenges in Designing Truly Conversational Agents Conversational agents promise conversational interaction but fail to deliver. Efforts often emulate functional rules from human speech, without considering key characteristics that conversation must encapsulate. Given its potential in supporting longterm humanagent relationships, it is paramount that HCI focuses efforts on delivering this promise. We aim to understand what people value in conversation and how this should manifest in agents. Findings from a series of semistructured interviews show people make a clear dichotomy between social and functional roles of conversation, emphasising the longterm dynamics of bond and trust along with the importance of context and relationship stage in the types of conversations they have. People fundamentally questioned the need for bond and common ground in agent communication, shifting to more utilitarian definitions of conversational qualities. Drawing on these findings we discuss key challenges for conversational agent design, most notably the need to redefine the design parameters for conversational agent interaction.

Improving Conversations: Lessons Learnt from Manual Analysis of Chatbot Dialogues Analysing and improving chatbot dialogues xe2x80x93 socalled chatbot xe2x80x98trainingxe2x80x99 xe2x80x93 is key to the successful implementation and maintenance of chatbots for customer service. Nevertheless, the details of this practice and what service providers may learn from the analysis of such dialogues is not investigated in current research on chatbots. As a first step towards bridging this gap in existing knowledge, we present a study of the qualitative analysis of chatbots dialogues in the context of the customer service department of a large telecom provider. In total 406 dialogues, randomly sampled from all chatbot dialogues during a fourweek period, were included in the analysis. The analysis concerned the chatbotxe2x80x99s ability to resolve customersxe2x80x99 requests, the quality in the chatbot dialogues, and suggestions for improvements of the chatbot knowledge base generated through the analysis. The findings shed light on characteristics of successful and unsuccessful chatbot dialogues and the kind of improvements that may be derived from such analysis. On the basis of the findings we summarize implications for theory and practice, and suggest future research.