Comparing informativeness of an NLG chatbot vs graphical app in diet-information domain
This work addresses the challenge of making diet information more accessible and understandable for users, though it is incremental as it builds on prior research in combining visualizations with text.
The study tackled the problem of communicating dietary information by comparing an NLG chatbot that combines charts and text with traditional static diet apps, finding that the chatbot significantly improved users' understanding and was rated as more useful and quicker to use.
Visual representation of data like charts and tables can be challenging to understand for readers. Previous work showed that combining visualisations with text can improve the communication of insights in static contexts, but little is known about interactive ones. In this work we present an NLG chatbot that processes natural language queries and provides insights through a combination of charts and text. We apply it to nutrition, a domain communication quality is critical. Through crowd-sourced evaluation we compare the informativeness of our chatbot against traditional, static diet-apps. We find that the conversational context significantly improved users' understanding of dietary data in various tasks, and that users considered the chatbot as more useful and quick to use than traditional apps.