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Eviza: A Natural Language Interface for Visual Analysis

Natural language interfaces for visualizations have emerged as a promising new way of interacting with data and performing analytics. Many of these systems have fundamental limitations. Most return minimally interactive visualizations in response to queries and often require experts to perform modeling for a set of predicted user queries before the systems are effective. Eviza provides a natural language interface for an interactive query dialog with an existing visualization rather than starting from a blank sheet and ask- ing closed-ended questions that return a single text answer or static visualization. The system employs a probabilistic grammar based approach with predefined rules that are dynamically updated based on the data from the visualization, as opposed to computationally intensive deep learning or knowledge based approaches. The result of an interaction is a change to the view (e.g., filtering, navigation, selection) providing graphical answers and ambiguity widgets to handle ambiguous queries and system defaults. There is also rich domain awareness of time, space, and quantitative reason- ing built in, and linking into existing knowledge bases for additional semantics. Eviza also supports pragmatics and exploring multi-modal interactions to help enhance the expressiveness of how users can ask questions about their data during the flow of visual analysis.

Research Papers:

Enamul Hoque, Vidya Setlur, Melanie Tory, and Isaac Dykeman, Applying Pragmatics Principles for Interaction with Visual Analytics, IEEE Transaactions on Visualization and Computer Graphics (Proc. VAST), Oct. 2017.

Vidya Setlur, Sarah E Battersby, Melanie Tory, Rich Gossweiler, and Angel X Chang, Eviza: A Natural Language Interface for Visual Analysis, Proc. User Interface Software and Technology, pp. 365-377, Oct. 2016.

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