Quality Controlled Paraphrase Generation
Elron Bandel, Ranit Aharonov, et al.
ACL 2022
We are developing an automated visualization system that helps users combine two or more existing information graphics to form an integrated view. To establish empirical foundations for building such a system, we designed and conducted two studies on Amazon Mechanical Turk to understand users' comprehension and preferences of composite visualization under different conditions (e.g., data and tasks). In Study 1, we collected more than 1,500 textual descriptions capturing about 500 participants' insights of given information graphics, which resulted in a task-oriented taxonomy of visual insights. In Study 2, we asked 240 participants to rank composite visualizations by their suitability for acquiring a given visual insight identified in Study 1, which resulted in ranked user preferences of visual compositions for acquiring each type of insight. In this article, we report the details of our two studies and discuss the broader implications of our crowdsourced research methodology and results to HCI-driven visualization research.© 2014 ACM.
Elron Bandel, Ranit Aharonov, et al.
ACL 2022
Atsuyoshi Nakamura, Naoki Abe
Electronic Commerce Research
Jonathan Malkin, Xiao Li, et al.
Computer Speech and Language
Opher Etzion
DEBS 2007