@inproceedings{inferring_vis_chalapathi_2024,
author = {Li*, Haotian and Chalapathi*, Nithin and Qu, Huamin and Cheung, Alvin and Parameswaran, Aditya G.},
title = {Inferring Visualization Intent from Conversation},
year = {2024},
isbn = {9798400704369},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3627673.3679589},
doi = {10.1145/3627673.3679589},
abstract = {During visual data analysis, users often explore visualizations one at a time, with each visualization leading to new directions of exploration. We consider a conversational approach to visualization, where users specify their needs at each step in natural language, with a visualization being returned in turn. Prior work has shown that visualization generation can be boiled down to the identification of visualization intent and visual encodings. Recognizing that the latter is a well-studied problem with standard solutions, we focus on the former, i.e., identifying visualization intent during conversation. We develop Luna, a framework that comprises a novel combination of language models adapted from BERT and rule-based inference, that together predict various aspects of visualization intent. We compare Luna with other conversational NL-to-visualization and NL-to-SQL approaches (adapted to visualization intent), including GPT-3.5 and GPT-4, and demonstrate that Luna has 14.3\% higher accuracy than the state-of-the-art. We also apply Luna to a usage scenario on a dataset of police misconduct, showcasing its benefits relative to other approaches.},
booktitle = {Proceedings of the 33rd ACM International Conference on Information and Knowledge Management},
pages = {1184-1194},
numpages = {11},
keywords = {conversational natural language interface, visualization recommendation},
location = {Boise, ID, USA},
series = {CIKM '24},
pdf = {inferring_visualization_chalapathi_2024.pdf},
preview = {inferring_visualization_chalapathi_2024_preview.png},
}