Arjun Srinivasan
Tableau Research, Seattle, Washington, United States
Natural language interfaces (NLIs) for data visualization are becoming increasingly popular both in academic research and in commercial software. Yet, there is a lack of empirical understanding of how people specify visualizations through natural language. We conducted an online study (N = 102), showing participants a series of visualizations and asking them to provide utterances they would pose to generate the displayed charts. From the responses, we curated a dataset of 893 utterances and characterized the utterances according to (1) their phrasing (e.g., commands, queries, questions) and (2) the information they contained (e.g., chart types, data aggregations). To help guide future research and development, we contribute this utterance dataset and discuss its applications toward the creation and benchmarking of NLIs for visualization.
https://doi.org/10.1145/3411764.3445400
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)