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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 15th International Conference on Natural Language Generation |
| Place of Publication | Waterville, Maine, USA and virtual meeting |
| Publisher | Association for Computational Linguistics |
| Pages | 156-185 |
| Number of pages | 30 |
| Publication status | Published - 1 Jul 2022 |