Towards Natural Language Interfaces for Data Visualization: A Survey

Shen, Leixian; Shen, Enya; Luo, Yuling; Yang, Xiaocong; Hu, Xuming; Zhang, Xiongshuai; Tai, Zhiwei; Wang, Jianmin

doi:10.1109/tvcg.2022.3148007

Cited by 67 publications

(19 citation statements)

References 239 publications

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“…Recently, Visualization-oriented Natural Language Interfaces (V-NLIs) are becoming a complementary input modality to traditional WIMP interaction [ 50 ]. V-NLIs can extract the user’s task by understanding the NL utterances since they may hint at the user’s analysis goals.…”

Section: Discussionmentioning

confidence: 99%

“…Further, the user’s occupation, social hotspots, and geographic information can be helpful to the personalized recommendation. In addition, a natural language interface can also greatly improve the user experience of the VisRec systems [ 35 , 36 , 50 ]. Knowledge graph to construct rules Draco [ 39 ] has formalized visualization design knowledge as constraints, but the knowledge is independent of each other, which is not conducive to knowledge reasoning.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Visual Data Analysis with Task-Based Recommendations

Shen

Tai

et al. 2022

Data Sci. Eng.

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General visualization recommendation systems typically make design decisions for the dataset automatically. However, most of them can only prune meaningless visualizations but fail to recommend targeted results. This paper contributes TaskVis, a task-oriented visualization recommendation system that allows users to select their tasks precisely on the interface. We first summarize a task base with 18 classical analytic tasks by a survey both in academia and industry. On this basis, we maintain a rule base, which extends empirical wisdom with our targeted modeling of the analytic tasks. Then, our rule-based approach enumerates all the candidate visualizations through answer set programming. After that, the generated charts can be ranked by four ranking schemes. Furthermore, we introduce a task-based combination recommendation strategy, leveraging a set of visualizations to give a brief view of the dataset collaboratively. Finally, we evaluate TaskVis through a series of use cases and a user study.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Visual Data Analysis with Task-Based Recommendations

Shen

Tai

et al. 2022

Data Sci. Eng.

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“…Related Surveys. The survey of Shen et al (2021) considers 55 visualization-oriented natural language interfaces (V-NLI) at the intersection of NLP and VIS. The work focuses on the applicability of natural language interfaces at the different steps of the information visualization pipeline by Kard et al (1999).…”

Section: Introductionmentioning

confidence: 99%

The Why and The How: A Survey on Natural Language Interaction in Visualization

Voigt¹,

Alaçam²,

Meuschke³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Natural language as a modality of interaction is becoming increasingly popular in the field of visualization. In addition to the popular query interfaces, other language-based interactions such as annotations, recommendations, explanations, or documentation experience growing interest. In this survey, we provide an overview of natural language-based interaction in the research area of visualization. We discuss a renowned taxonomy of visualization tasks and classify 119 related works to illustrate the stateof-the-art of how current natural language interfaces support their performance. We examine applied NLP methods and discuss humanmachine dialogue structures with a focus on initiative, duration, and communicative functions in recent visualization-oriented dialogue interfaces. Based on this overview, we point out interesting areas for the future application of NLP methods in the field of visualization.

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“…As discussed in §1.1, the interplay between DTG systems and visual analytics may offer exciting avenues for data-comprehension systems [339], [340].…”

mentioning

confidence: 99%

Innovations in Neural Data-to-text Generation

Sharma¹,

Gogineni²,

Ramakrishnan³

2022

Preprint

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The neural boom that has sparked natural language processing (NLP) research through the last decade has similarly led to significant innovations in data-to-text generation (DTG). This survey offers a consolidated view into the neural DTG paradigm with a structured examination of the approaches, benchmark datasets, and evaluation protocols. This survey draws boundaries separating DTG from the rest of the natural language generation (NLG) landscape, encompassing an up-to-date synthesis of the literature, and highlighting the stages of technological adoption from within and outside the greater NLG umbrella. With this holistic view, we highlight promising avenues for DTG research that not only focus on the design of linguistically capable systems but also systems that exhibit fairness and accountability.Index Terms-Data-to-text generation (DTG), natural language generation (NLG) ! 1. This survey exclusively focuses on academic innovations for datato-text generation as the technologies underlying commercial frameworks are often proprietary.

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Towards Natural Language Interfaces for Data Visualization: A Survey

Cited by 67 publications

References 239 publications

Visual Data Analysis with Task-Based Recommendations

Visual Data Analysis with Task-Based Recommendations

The Why and The How: A Survey on Natural Language Interaction in Visualization

Innovations in Neural Data-to-text Generation

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