Comparing informativeness of an NLG chatbot vs graphical app in diet-information domain

TL;DR

This study introduces an NLG chatbot for diet information that combines natural language processing with visual data, significantly enhancing user understanding and perceived usefulness over traditional static diet apps.

Contribution

The paper presents a novel interactive chatbot that integrates natural language queries with visual data to improve dietary data communication.

Findings

Chatbot significantly improved user understanding of dietary data.

Users found the chatbot more useful and quicker to use.

Interactive context enhanced communication effectiveness.

Abstract

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.