Eviza: A Natural Language Interface for Visual Analysis

By Vidya Setlur, Sarah E. Battersby, Melanie Tory, Rich Gossweiler, Angel X. Chang. ACM Symposium on User Interface Software and Technology, 2016

302 citations recorded by the source corpus.

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 asking 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 reasoning 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.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d5480773999268984fcc0d23e9b1cab20fea3d71

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Citations in the corpus

FLIN: A Flexible Natural Language Interface for Web Navigation (2020, unknown): “analysis (Setlur et al., 2016; Utama et al., 2018; Lawrence and Riezler, 2016; Gao et al., 2015), robot navigation (Artzi and Zettlemoyer, 2013; Tellex et al.”
Photon: A Robust Cross-Domain Text-to-SQL System (2020, unknown): “Jagadish, 2014; Setlur et al., 2016, 2019).”
Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco (2018, background): “Natural language interfaces may be better suited for communicating user intent [13, 53, 60], which could then be expressed as Draco constraints.”
ChartDialogs: Plotting from Natural Language Instructions (2020, background): “This category of NLIs has been studied in Human Computer Interaction and related areas (Gao et al., 2015; Setlur et al., 2016; Srini-vasan and Stasko, 2017; Yu and Silva, 2019; Sun et al., 2010).”
An Application-Independent Approach to Building Task-Oriented Chatbots with Interactive Continual Learning (2020, methodology): “Besides, [32, 41, 17, 14] built NLIs for visual data analysis.”