How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational Behavior
Vidya Setlur, Melanie Tory

TL;DR
This paper investigates how Gricean Maxims can guide the design of analytical chatbots, exploring user expectations and system behaviors through Wizard of Oz studies and analyzing different interface variants for effective data-driven conversations.
Contribution
It introduces a framework based on Gricean Maxims for designing analytical chatbots and provides empirical insights into user preferences and system performance across different interface types.
Findings
Preferences for intent interpretation vary by interface
Voice + chart interface enhances data understanding
Trust is influenced by system response appropriateness
Abstract
Chatbots have garnered interest as conversational interfaces for a variety of tasks. While general design guidelines exist for chatbot interfaces, little work explores analytical chatbots that support conversing with data. We explore Gricean Maxims to help inform the basic design of effective conversational interaction. We also draw inspiration from natural language interfaces for data exploration to support ambiguity and intent handling. We ran Wizard of Oz studies with 30 participants to evaluate user expectations for text and voice chatbot design variants. Results identified preferences for intent interpretation and revealed variations in user expectations based on the interface affordances. We subsequently conducted an exploratory analysis of three analytical chatbot systems (text + chart, voice + chart, voice-only) that implement these preferred design variants. Empirical evidence…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAI in Service Interactions · Speech and dialogue systems · Topic Modeling
