Do What I Mean, Not What I Say! Design Considerations for Supporting Intent and Context in Analytical Conversation Natural language can be a useful modality for creating and interacting with visualizations but users often have unrealistic expectations about the intelligence of natural language systems. The gulf between user expectations and system capabilities may lead to a disappointing user experience. So xe2x80x94 if we want to engineer a natural language system, what are the requirements around system intelligence? This work takes a retrospective look at how we answered this question in the design of Ask Data, a natural language interaction feature for Tableau. We examine two factors contributing to perceived system intelligence: the systemu0027s ability to understand the analytic intent behind an input utterance and the ability to interpret an utterance contextually (i.e. taking into account the current visualization state and recent actions). Our aim was to understand the ways in which a system would need to support these two aspects of intelligence to enable a positive user experience. We first describe a predesign Wizard of Oz study that offered insight into this question and narrowed the space of designs under consideration. We then reflect on the impact of this study on system development, examining how design implications from the study played out in practice. Our work contributes insights for the design of natural language interaction in visual analytics as well as a reflection on the value of predesign empirical studies in the development of visual analytic systems.

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.