InChorus: Designing Consistent Multimodal Interactions for Data Visualization on Tablet Devices
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Abstract (arXiv)
While tablet devices are a promising platform for data visualization, supporting consistent interactions across different types of visualizations on tablets remains an open challenge. In this paper, we present multimodal interactions that function consistently across different visualizations, supporting common operations during visual data analysis. By considering standard interface elements (e.g., axes, marks) and grounding our design in a set of core concepts including operations, parameters, targets, and instruments, we systematically develop interactions applicable to different visualization types. To exemplify how the proposed interactions collectively facilitate data exploration, we employ them in a tablet-based system, InChorus that supports pen, touch, and speech input. Based on a study with 12 participants performing replication and fact-checking tasks with InChorus, we discuss how participants adapted to using multimodal input and highlight considerations for future multimodal visualization systems.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jan 2020.
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Citing papers per year: 2022: 2; 2023: 3; 2024: 4; 2025: 0; 2026: 3.
12 papers.
- 2023Natural Language Interfaces for Tabular Data Querying and Visualization: A Surveycited by 27
- 2022Chart Question Answering: State of the Art and Future Directionscited by 26
- 2024Generative AI for Visualization: State of the Art and Future Directionscited by 23
- 2023XNLI: Explaining and Diagnosing NLI-based Visual Data Analysiscited by 19
- 2024Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directionscited by 6
- 2022MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speechcited by 5
- 2026From Words to Widgets for Controllable LLM Generationcited by 5
- 2026MultiVis-Agent: A Multi-Agent Framework with Logic Rules for Reliable and Comprehensive Cross-Modal Data Visualizationcited by 3
- 2024Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizationscited by 2
- 2024SHAPE-IT: Exploring Text-to-Shape-Display for Generative Shape-Changing Behaviors with LLMscited by 1
- 2023Speakerly: A Voice-based Writing Assistant for Text Compositioncited by 0
- 2026Bridging Natural Language and Interactive What-If Interfaces via LLM-Generated Declarative Specificationscited by 0
- 2026From Words to Widgets for Controllable LLM Generation13 Apr 2026
- 2026Bridging Natural Language and Interactive What-If Interfaces via LLM-Generated Declarative Specifications8 Apr 2026
- 2026MultiVis-Agent: A Multi-Agent Framework with Logic Rules for Reliable and Comprehensive Cross-Modal Data Visualization26 Jan 2026
- 2024Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions29 Sep 2024
- 2024Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations27 Sep 2024
- 2024SHAPE-IT: Exploring Text-to-Shape-Display for Generative Shape-Changing Behaviors with LLMs10 Sep 2024
- 2024Generative AI for Visualization: State of the Art and Future Directions28 Apr 2024
- 2023Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey27 Oct 2023
- 2023Speakerly: A Voice-based Writing Assistant for Text Composition24 Oct 2023
- 2023XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis25 Jan 2023
- 2022Chart Question Answering: State of the Art and Future Directions8 May 2022
- 2022MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech1 Apr 2022