Towards Natural Language Interfaces for Data Visualization: A Survey

Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary input modality to direct manipulation for visual analytics can provide an engaging user experience. It enables users to focus on their tasks rather than worrying about operating the interface to visualization tools. In the past two decades, leveraging advanced natural language processing technologies, numerous V-NLI systems have been developed both within academic research and commercial software, especially in recent years. In this article, we conduct a comprehensive review of the existing V-NLIs. In order to classify each paper, we develop categorical dimensions based on a classic information visualization pipeline with the extension of a V-NLI layer. The following seven stages are used: query understanding, data transformation, visual mapping, view transformation, human interaction, context management, and presentation. Finally, we also shed light on several promising directions for future work in the community.

[1]  Christoph Trattner,et al.  Towards a Recommender Engine for Personalized Visualizations , 2015, UMAP.

[2]  Michael Rohs,et al.  Valletto: A Multimodal Interface for Ubiquitous Visual Analytics , 2018, CHI Extended Abstracts.

[3]  Xiaoru Yuan,et al.  ADVISor: Automatic Visualization Answer for Natural-Language Question on Tabular Data , 2021, 2021 IEEE 14th Pacific Visualization Symposium (PacificVis).

[4]  Yiting Wang,et al.  PathViewer: Visualizing Pathways through Student Data , 2017, CHI.

[5]  Arvind Satyanarayan,et al.  Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization , 2016, IEEE Transactions on Visualization and Computer Graphics.

[6]  Jarke J. van Wijk,et al.  Small Multiples, Large Singles: A New Approach for Visual Data Exploration , 2013, Comput. Graph. Forum.

[7]  Jie Li,et al.  Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling , 2020, IEEE Transactions on Visualization and Computer Graphics.

[8]  Karrie Karahalios,et al.  ShapeSearch: Flexible Pattern-based Querying of Trend Line Visualizations , 2018, Proc. VLDB Endow..

[9]  Risto Miikkulainen,et al.  Subsymbolic natural language processing - an integrated model of scripts, lexicon, and memory , 1993, Neural network modeling and connectionism.

[10]  Sandra Carberry,et al.  Generating Summaries of Line Graphs , 2014, INLG.

[11]  NAVID YAGHMAZADEH,et al.  SQLizer: query synthesis from natural language , 2017, Proc. ACM Program. Lang..

[12]  Hannah Bast,et al.  More Accurate Question Answering on Freebase , 2015, CIKM.

[13]  Panagiotis D. Ritsos,et al.  VRIA: A Web-Based Framework for Creating Immersive Analytics Experiences , 2020, IEEE Transactions on Visualization and Computer Graphics.

[14]  Roland Vollgraf,et al.  Contextual String Embeddings for Sequence Labeling , 2018, COLING.

[15]  Dmitry Mouromtsev,et al.  A Comparative Evaluation of Visual and Natural Language Question Answering Over Linked Data , 2019, KEOD.

[16]  Fei Li,et al.  Understanding Natural Language Queries over Relational Databases , 2016, SGMD.

[17]  Abhinav Kumar,et al.  Towards a dialogue system that supports rich visualizations of data , 2016, SIGDIAL Conference.

[18]  Sana Malik,et al.  Generating Accurate Caption Units for Figure Captioning , 2021, WWW.

[19]  Dominik Moritz,et al.  Dziban: Balancing Agency & Automation in Visualization Design via Anchored Recommendations , 2020, CHI.

[20]  Mark Chen,et al.  Language Models are Few-Shot Learners , 2020, NeurIPS.

[21]  Weiwei Cui,et al.  Retrieve-Then-Adapt: Example-based Automatic Generation for Proportion-related Infographics , 2020, IEEE Transactions on Visualization and Computer Graphics.

[22]  You Wu,et al.  TURL , 2020, Proc. VLDB Endow..

[23]  Adrien Fonnet,et al.  Survey of Immersive Analytics , 2021, IEEE Transactions on Visualization and Computer Graphics.

[24]  Maneesh Agrawala,et al.  Graphical Overlays: Using Layered Elements to Aid Chart Reading , 2012, IEEE Transactions on Visualization and Computer Graphics.

[25]  Marti Hearst,et al.  Toward Interface Defaults for Vague Modifiers in Natural Language Interfaces for Visual Analysis , 2019, 2019 IEEE Visualization Conference (VIS).

[26]  Yan Song,et al.  Knowledge-aware Pronoun Coreference Resolution , 2019, ACL.

[27]  John T. Stasko,et al.  Natural Language Interfaces for Data Analysis with Visualization: Considering What Has and Could Be Asked , 2017, EuroVis.

[28]  Abdul Quamar,et al.  Natural Language Querying of Complex Business Intelligence Queries , 2019, SIGMOD Conference.

[29]  Dawn Xiaodong Song,et al.  SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning , 2017, ArXiv.

[30]  Jeffrey Heer,et al.  Reverse‐Engineering Visualizations: Recovering Visual Encodings from Chart Images , 2017, Comput. Graph. Forum.

[31]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[32]  Pat Hanrahan,et al.  Polaris: A System for Query, Analysis, and Visualization of Multidimensional Relational Databases , 2002, IEEE Trans. Vis. Comput. Graph..

[33]  Ajay Joshi,et al.  LEAF-QA: Locate, Encode & Attend for Figure Question Answering , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[34]  John T. Stasko,et al.  Data Animator: Authoring Expressive Animated Data Graphics , 2021, CHI.

[35]  Silvia Miksch,et al.  Task Cube: A three-dimensional conceptual space of user tasks in visualization design and evaluation , 2016, Inf. Vis..

[36]  Bongshin Lee,et al.  Interweaving Multimodal Interaction With Flexible Unit Visualizations for Data Exploration , 2020, IEEE Transactions on Visualization and Computer Graphics.

[37]  Mary Czerwinski,et al.  Understanding the verbal language and structure of end-user descriptions of data visualizations , 2012, CHI.

[38]  S. Muthukrishnan,et al.  EXACTA: Explainable Column Annotation , 2021, KDD.

[39]  Birgitta König-Ries,et al.  Towards Visualization Recommendation - A Semi- Automated Domain-Specific Learning Approach , 2015, GvD.

[40]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[41]  Xuedi Qin,et al.  Synthesizing Natural Language to Visualization (NL2VIS) Benchmarks from NL2SQL Benchmarks , 2021, SIGMOD Conference.

[42]  Ryan A. Rossi,et al.  Learning to Recommend Visualizations from Data , 2021, KDD.

[43]  Omer Levy,et al.  Coreference Resolution without Span Representations , 2021, ACL.

[44]  Alexander M. Rush,et al.  Challenges in Data-to-Document Generation , 2017, EMNLP.

[45]  H. V. Jagadish,et al.  NaLIR: an interactive natural language interface for querying relational databases , 2014, SIGMOD Conference.

[46]  Robert Kincaid,et al.  Nicky: Toward a Virtual Assistant for Test and Measurement Instrument Recommendations , 2017, 2017 IEEE 11th International Conference on Semantic Computing (ICSC).

[47]  Xiaoru Yuan,et al.  AutoCaption: An Approach to Generate Natural Language Description from Visualization Automatically , 2020, 2020 IEEE Pacific Visualization Symposium (PacificVis).

[48]  Yong Xu,et al.  QuickInsights: Quick and Automatic Discovery of Insights from Multi-Dimensional Data , 2019, SIGMOD Conference.

[49]  Luis Gustavo Nonato,et al.  Multidimensional Projection for Visual Analytics: Linking Techniques with Distortions, Tasks, and Layout Enrichment , 2019, IEEE Transactions on Visualization and Computer Graphics.

[50]  Yong Wang,et al.  Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline , 2019, IEEE Transactions on Visualization and Computer Graphics.

[51]  Brian L. Price,et al.  DVQA: Understanding Data Visualizations via Question Answering , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[52]  Arvind Satyanarayan,et al.  VisuaLint: Sketchy In Situ Annotations of Chart Construction Errors , 2020, Comput. Graph. Forum.

[53]  Fei Li,et al.  Constructing an Interactive Natural Language Interface for Relational Databases , 2014, Proc. VLDB Endow..

[54]  Maneesh Agrawala,et al.  Towards Understanding How Readers Integrate Charts and Captions: A Case Study with Line Charts , 2021, CHI.

[55]  Bei-Bei Huang,et al.  A Natural Language Database Interface Based on a Probabilistic Context Free Grammar , 2008, IEEE International Workshop on Semantic Computing and Systems.

[56]  Giuseppe Carenini,et al.  Neural Data-Driven Captioning of Time-Series Line Charts , 2020, AVI.

[57]  Raimund Dachselt,et al.  MIRIA: A Mixed Reality Toolkit for the In-Situ Visualization and Analysis of Spatio-Temporal Interaction Data , 2021, CHI.

[58]  Lei Liu,et al.  Application of Hidden Markov Model in SQL Injection Detection , 2017, 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC).

[59]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[60]  Andreas Schreiber,et al.  A Conversational User Interface for Software Visualization , 2017, 2017 IEEE Working Conference on Software Visualization (VISSOFT).

[61]  Aditya G. Parameswaran,et al.  SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics , 2015, Proc. VLDB Endow..

[62]  Yuchen Zhang,et al.  CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes , 2012, EMNLP-CoNLL Shared Task.

[63]  Lina Yao,et al.  Deep Learning Based Recommender System , 2017, ACM Comput. Surv..

[64]  Diyi Yang,et al.  The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics , 2021, GEM.

[65]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[66]  Tim Kraska,et al.  VizML: A Machine Learning Approach to Visualization Recommendation , 2018, CHI.

[67]  Mingjie Tang,et al.  A Natural-language-based Visual Query Approach of Uncertain Human Trajectories , 2019, IEEE Transactions on Visualization and Computer Graphics.

[68]  Candace L. Sidner,et al.  Attention, Intentions, and the Structure of Discourse , 1986, CL.

[69]  Alvin Cheung,et al.  Falx: Synthesis-Powered Visualization Authoring , 2021, CHI.

[70]  Kanit Wongsuphasawat,et al.  Voyager 2: Augmenting Visual Analysis with Partial View Specifications , 2017, CHI.

[71]  Thomas Muller,et al.  TaPas: Weakly Supervised Table Parsing via Pre-training , 2020, ACL.

[72]  James Pustejovsky,et al.  TimeML: Robust Specification of Event and Temporal Expressions in Text , 2003, New Directions in Question Answering.

[73]  Walter J. Scheirer,et al.  Coupling Story to Visualization: Using Textual Analysis as a Bridge Between Data and Interpretation , 2017, IUI.

[74]  M. Sheelagh T. Carpendale,et al.  Beyond Mouse and Keyboard: Expanding Design Considerations for Information Visualization Interactions , 2012, IEEE Transactions on Visualization and Computer Graphics.

[75]  Jeffrey Heer,et al.  ReVision: automated classification, analysis and redesign of chart images , 2011, UIST.

[76]  Li Gong,et al.  Enhanced Transformer Model for Data-to-Text Generation , 2019, EMNLP.

[77]  Jock D. Mackinlay,et al.  Automating the design of graphical presentations of relational information , 1986, TOGS.

[78]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL.

[79]  Daniel Perry,et al.  VizDeck: self-organizing dashboards for visual analytics , 2012, SIGMOD Conference.

[80]  Bowen Yu,et al.  FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System , 2019, IEEE Transactions on Visualization and Computer Graphics.

[81]  Vidya Setlur,et al.  Eviza: A Natural Language Interface for Visual Analysis , 2016, UIST.

[82]  Vidya Setlur,et al.  Inferencing underspecified natural language utterances in visual analysis , 2019, IUI.

[83]  Johanna D. Moore,et al.  Describing Complex Charts in Natural Language: A Caption Generation System , 1998, CL.

[84]  Alex Endert,et al.  Augmenting Visualizations with Interactive Data Facts to Facilitate Interpretation and Communication , 2019, IEEE Transactions on Visualization and Computer Graphics.

[85]  Chengliang Chai,et al.  Natural Language to Visualization by Neural Machine Translation , 2021, IEEE Transactions on Visualization and Computer Graphics.

[86]  Christophe Hurter,et al.  Data Visceralization: Enabling Deeper Understanding of Data Using Virtual Reality , 2021, IEEE Transactions on Visualization and Computer Graphics.

[87]  Tao Yu,et al.  Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task , 2018, EMNLP.

[88]  Yuchen Zhang,et al.  Macro Grammars and Holistic Triggering for Efficient Semantic Parsing , 2017, EMNLP.

[89]  Maximilian Speicher,et al.  MRAT: The Mixed Reality Analytics Toolkit , 2020, CHI.

[90]  Dieter Schmalstieg,et al.  Grand Challenges in Immersive Analytics , 2021, CHI.

[91]  Andries van Dam,et al.  Post-WIMP user interfaces , 1997, CACM.

[92]  Kathleen F. McCoy,et al.  Generating Textual Summaries of Bar Charts , 2008, INLG.

[93]  Scott Barlowe,et al.  Click2Annotate: Automated Insight Externalization with rich semantics , 2010, 2010 IEEE Symposium on Visual Analytics Science and Technology.

[94]  Maneesh Agrawala,et al.  Crosscast: Adding Visuals to Audio Travel Podcasts , 2020, UIST.

[95]  John T. Stasko,et al.  VisIRR: Visual analytics for information retrieval and recommendation with large-scale document data , 2014, 2014 IEEE Conference on Visual Analytics Science and Technology (VAST).

[96]  Chenhui Chu,et al.  A Survey of Multilingual Neural Machine Translation , 2019, ACM Comput. Surv..

[97]  Leilani Battle,et al.  A Structured Review of Data Management Technology for Interactive Visualization and Analysis , 2020, IEEE Transactions on Visualization and Computer Graphics.

[98]  Kwan-Liu Ma,et al.  MeetingVis: Visual Narratives to Assist in Recalling Meeting Context and Content , 2018, IEEE Transactions on Visualization and Computer Graphics.

[99]  Shi Han,et al.  Table2Charts: Recommending Charts by Learning Shared Table Representations , 2020, KDD.

[100]  Weiwei Cui,et al.  Learning to Automate Chart Layout Configurations Using Crowdsourced Paired Comparison , 2021, CHI.

[101]  Ioannis Vlahavas,et al.  A Neural Entity Coreference Resolution Review , 2021, Expert Syst. Appl..

[102]  Lucio Davide Spano,et al.  Inspecting Data Using Natural Language Queries , 2020, ICCSA.

[103]  Yun Wang,et al.  Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements , 2019, IEEE Transactions on Visualization and Computer Graphics.

[104]  Sungahn Ko,et al.  Thumbnails for Data Stories: A Survey of Current Practices , 2019, 2019 IEEE Visualization Conference (VIS).

[105]  Bongshin Lee,et al.  DIY: Assessing the Correctness of Natural Language to SQL Systems , 2021, IUI.

[106]  Xifeng Yan,et al.  DialSQL: Dialogue Based Structured Query Generation , 2018, ACL.

[107]  Abraham Bernstein,et al.  A comparative survey of recent natural language interfaces for databases , 2019, The VLDB Journal.

[108]  Maneesh Agrawala,et al.  Answering Questions about Charts and Generating Visual Explanations , 2020, CHI.

[109]  M. Corio,et al.  Generation of texts for information graphics , 1999 .

[110]  Tobias Höllerer,et al.  ChartAccent: Annotation for data-driven storytelling , 2017, 2017 IEEE Pacific Visualization Symposium (PacificVis).

[111]  Bongshin Lee,et al.  CAST: Authoring Data-Driven Chart Animations , 2021, CHI.

[112]  Luis A. Guerrero,et al.  Alexa vs. Siri vs. Cortana vs. Google Assistant: A Comparison of Speech-Based Natural User Interfaces , 2017 .

[113]  Barbara Tversky,et al.  Animation: can it facilitate? , 2002, Int. J. Hum. Comput. Stud..

[114]  Nadia Siddiqui,et al.  ConVisQA: A Natural Language Interface for Visually Exploring Online Conversations , 2020, 2020 24th International Conference Information Visualisation (IV).

[115]  Frédo Durand,et al.  Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics , 2018, ArXiv.

[116]  Luke S. Zettlemoyer,et al.  Higher-Order Coreference Resolution with Coarse-to-Fine Inference , 2018, NAACL.

[117]  Wang Ling,et al.  Reference-Aware Language Models , 2016, EMNLP.

[118]  James R. Eagan,et al.  Low-level components of analytic activity in information visualization , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[119]  Kathleen F. McCoy,et al.  Summarizing Information Graphics Textually , 2012, CL.

[120]  Michael Gleicher,et al.  Scatterplots: Tasks, Data, and Designs , 2018, IEEE Transactions on Visualization and Computer Graphics.

[121]  Eser Kandogan,et al.  Just-in-time annotation of clusters, outliers, and trends in point-based data visualizations , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[122]  Zhen Wen,et al.  Behavior-driven visualization recommendation , 2009, IUI.

[123]  Gitte Lindgaard,et al.  Evaluating a tool for improving accessibility to charts and graphs , 2010, ASSETS '10.

[124]  Michael S. Bernstein,et al.  Iris: A Conversational Agent for Complex Tasks , 2017, CHI.

[125]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[126]  Michelle X. Zhou,et al.  An optimization-based approach to dynamic visual context management , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[127]  Hongming Zhang,et al.  A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution in English , 2020, CRAC.

[128]  Ricardo Langner,et al.  MARVIS: Combining Mobile Devices and Augmented Reality for Visual Data Analysis , 2021, CHI.

[129]  Maryam Nafari,et al.  Query2Question: Translating Visualization Interaction into Natural Language , 2015, IEEE Transactions on Visualization and Computer Graphics.

[130]  Omer Levy,et al.  SpanBERT: Improving Pre-training by Representing and Predicting Spans , 2019, TACL.

[131]  Yiwen Sun,et al.  Articulate: A Semi-automated Model for Translating Natural Language Queries into Meaningful Visualizations , 2010, Smart Graphics.

[132]  John T. Stasko,et al.  Orko: Facilitating Multimodal Interaction for Visual Exploration and Analysis of Networks , 2018, IEEE Transactions on Visualization and Computer Graphics.

[133]  Donald Kossmann,et al.  SODA: Generating SQL for Business Users , 2012, Proc. VLDB Endow..

[134]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[135]  Brent J. Hecht,et al.  NewsViews: an automated pipeline for creating custom geovisualizations for news , 2014, CHI.

[136]  Yonatan Belinkov,et al.  Analysis Methods in Neural Language Processing: A Survey , 2018, TACL.

[137]  Mitsunori Matsushita,et al.  An interactive visualization method of numerical data based on natural language requirements , 2004, Int. J. Hum. Comput. Stud..

[138]  Abdul Quamar,et al.  State of the Art and Open Challenges in Natural Language Interfaces to Data , 2020, SIGMOD Conference.

[139]  Aditya G. Parameswaran,et al.  SEEDB: Automatically Generating Query Visualizations , 2014, Proc. VLDB Endow..

[140]  Karrie Karahalios,et al.  Deconstructing Categorization in Visualization Recommendation: A Taxonomy and Comparative Study , 2021, IEEE Transactions on Visualization and Computer Graphics.

[141]  Tim Kraska,et al.  VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository , 2019, CHI.

[142]  Kanit Wongsuphasawat,et al.  Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations , 2016, IEEE Transactions on Visualization and Computer Graphics.

[143]  Tamara Munzner,et al.  VizCommender: Computing Text-Based Similarity in Visualization Repositories for Content-Based Recommendations , 2020, IEEE Transactions on Visualization and Computer Graphics.

[144]  Jeffrey Heer,et al.  Narrative Visualization: Telling Stories with Data , 2010, IEEE Transactions on Visualization and Computer Graphics.

[145]  Yingcai Wu,et al.  What Makes a Data-GIF Understandable? , 2020, IEEE Transactions on Visualization and Computer Graphics.

[146]  Klaus Miesenberger,et al.  AUDiaL: A Natural Language Interface to Make Statistical Charts Accessible to Blind Persons , 2020, ICCHP.

[147]  Umar Farooq Minhas,et al.  ATHENA: An Ontology-Driven System for Natural Language Querying over Relational Data Stores , 2016, Proc. VLDB Endow..

[148]  Tarique Siddiqui From Sketching to Natural Language: Expressive Visual Querying for Accelerating Insight , 2021 .

[149]  Jignesh M. Patel,et al.  Ava: From Data to Insights Through Conversations , 2017, CIDR.

[150]  Tamara Munzner,et al.  A Multi-Level Typology of Abstract Visualization Tasks , 2013, IEEE Transactions on Visualization and Computer Graphics.

[151]  Cristina Conati,et al.  Constructing Models of User and Task Characteristics from Eye Gaze Data for User-Adaptive Information Highlighting , 2015, AAAI.

[152]  Pat Hanrahan,et al.  Show Me: Automatic Presentation for Visual Analysis , 2007, IEEE Transactions on Visualization and Computer Graphics.

[153]  Hrituraj Singh,et al.  STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering , 2020, EMNLP.

[154]  Çagatay Demiralp,et al.  Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks , 2018, IEEE Computer Graphics and Applications.

[155]  Changhe Tu,et al.  Data-Driven Mark Orientation for Trend Estimation in Scatterplots , 2021, CHI.

[156]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[157]  Frédo Durand,et al.  Understanding Infographics through Textual and Visual Tag Prediction , 2017, ArXiv.

[158]  Dongmei Zhang,et al.  MultiVision: Designing Analytical Dashboards with Deep Learning Based Recommendation , 2021, IEEE Transactions on Visualization and Computer Graphics.

[159]  John Stasko,et al.  NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries , 2020, IEEE Transactions on Visualization and Computer Graphics.

[160]  Guodao Sun,et al.  A survey on automatic infographics and visualization recommendations , 2020, Vis. Informatics.

[161]  Subhadip Maji,et al.  DCoM: A Deep Column Mapper for Semantic Data Type Detection , 2021, ArXiv.

[162]  Tingfa Xu,et al.  LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators , 2019, ICLR.

[163]  Jignesh M. Patel,et al.  A Natural Language Interface for Dissemination of Reproducible Biomedical Data Science , 2018, MICCAI.

[164]  David Grangier,et al.  Neural Text Generation from Structured Data with Application to the Biography Domain , 2016, EMNLP.

[165]  Vidya Setlur,et al.  Do What I Mean, Not What I Say! Design Considerations for Supporting Intent and Context in Analytical Conversation , 2019, 2019 IEEE Conference on Visual Analytics Science and Technology (VAST).

[166]  Daniel F. Keefe,et al.  How to Ask What to Say?: Strategies for Evaluating Natural Language Interfaces for Data Visualization , 2020, IEEE Computer Graphics and Applications.

[167]  Haixun Wang,et al.  A Natural Language Interface for Database: Achieving Transfer-learnability Using Adversarial Method for Question Understanding , 2020, 2020 IEEE 36th International Conference on Data Engineering (ICDE).

[168]  Richard Socher,et al.  Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning , 2018, ArXiv.

[169]  Jeffrey Heer,et al.  Wrangler: interactive visual specification of data transformation scripts , 2011, CHI.

[170]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[171]  Yang Shi,et al.  Calliope: Automatic Visual Data Story Generation from a Spreadsheet , 2020, IEEE Transactions on Visualization and Computer Graphics.

[172]  Guoliang Li,et al.  DeepEye: Towards Automatic Data Visualization , 2018, 2018 IEEE 34th International Conference on Data Engineering (ICDE).

[173]  Tim Kraska,et al.  Sherlock: A Deep Learning Approach to Semantic Data Type Detection , 2019, KDD.

[174]  Xiaoru Yuan,et al.  Automatic Annotation Synchronizing with Textual Description for Visualization , 2020, CHI.

[175]  Guoliang Li,et al.  DeepEye: Creating Good Data Visualizations by Keyword Search , 2018, SIGMOD Conference.

[176]  Yong Wang,et al.  KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation , 2021, IEEE Transactions on Visualization and Computer Graphics.

[177]  Thomas Muller,et al.  Understanding tables with intermediate pre-training , 2020, FINDINGS.

[178]  Cristina Conati,et al.  User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities , 2013, IUI '13.

[179]  Alex Endert,et al.  Task-Based Effectiveness of Basic Visualizations , 2017, IEEE Transactions on Visualization and Computer Graphics.

[180]  Barbara Di Eugenio,et al.  Multimodal Coreference Resolution for Exploratory Data Visualization Dialogue: Context-Based Annotation and Gesture Identification , 2017 .

[181]  Yong Wang,et al.  A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization , 2020, IEEE Transactions on Visualization and Computer Graphics.

[182]  Niklas Elmqvist,et al.  DataSite: Proactive visual data exploration with computation of insight-based recommendations , 2018, Inf. Vis..

[183]  Omer Levy,et al.  BERT for Coreference Resolution: Baselines and Analysis , 2019, EMNLP/IJCNLP.

[184]  Mihai Surdeanu,et al.  The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.

[185]  Yan Gao,et al.  Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation , 2019, ACL.

[186]  Sonia Bergamaschi,et al.  Combining user and database perspective for solving keyword queries over relational databases , 2016, Inf. Syst..

[187]  Guy Lapalme,et al.  PostGraphe: A System for the Generation of Statistical Graphics and Text , 1996, INLG.

[188]  Cláudio T. Silva,et al.  VisFlow - Web-based Visualization Framework for Tabular Data with a Subset Flow Model , 2017, IEEE Transactions on Visualization and Computer Graphics.

[189]  Nazli Ikizler-Cinbis,et al.  RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes , 2018, EMNLP.

[190]  John T. Stasko,et al.  Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations , 2021, CHI.

[191]  Vidya Setlur,et al.  Sentifiers: Interpreting Vague Intent Modifiers in Visual Analysis using Word Co-occurrence and Sentiment Analysis , 2020, 2020 IEEE Visualization Conference (VIS).

[192]  Ben Shneiderman,et al.  A Rank-by-Feature Framework for Interactive Exploration of Multidimensional Data , 2005, Inf. Vis..

[193]  Abdul Quamar,et al.  An Ontology-Based Conversation System for Knowledge Bases , 2020, SIGMOD Conference.

[194]  Mitsunori Matsushita,et al.  Answering it with Charts: Dialogue in Natural Language and Charts , 2002, COLING.

[195]  H. V. Jagadish,et al.  Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).

[196]  Ben Shneiderman,et al.  Readings in information visualization - using vision to think , 1999 .

[197]  Dae Hyun Kim,et al.  Sneak Pique: Exploring Autocompletion as a Data Discovery Scaffold for Supporting Visual Analysis , 2020, UIST.

[198]  Kwan-Liu Ma,et al.  Temporal Summary Images: An Approach to Narrative Visualization via Interactive Annotation Generation and Placement , 2017, IEEE Transactions on Visualization and Computer Graphics.

[199]  John Stasko,et al.  Touch? Speech? or Touch and Speech? Investigating Multimodal Interaction for Visual Network Exploration and Analysis , 2020, IEEE Transactions on Visualization and Computer Graphics.

[200]  HeerJeffrey,et al.  D3 Data-Driven Documents , 2011 .

[201]  Russ Burtner,et al.  Mixed-initiative visual analytics using task-driven recommendations , 2015, 2015 IEEE Conference on Visual Analytics Science and Technology (VAST).

[202]  Michael Stonebraker,et al.  The Role of Latency and Task Complexity in Predicting Visual Search Behavior , 2020, IEEE Transactions on Visualization and Computer Graphics.

[203]  Leilani Battle,et al.  Vis Ex Machina: An Analysis of Trust in Human versus Algorithmically Generated Visualization Recommendations , 2021, CHI.

[204]  Christopher D. Manning,et al.  Stanza: A Python Natural Language Processing Toolkit for Many Human Languages , 2020, ACL.

[205]  Jeffrey Heer,et al.  Gemini: A Grammar and Recommender System for Animated Transitions in Statistical Graphics , 2020, IEEE Transactions on Visualization and Computer Graphics.

[206]  Dominik Moritz,et al.  AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization , 2021, IEEE Transactions on Visualization and Computer Graphics.

[207]  Nan Chen,et al.  Task-Oriented Optimal Sequencing of Visualization Charts , 2019, 2019 IEEE Visualization in Data Science (VDS).

[208]  Michel Mitri,et al.  Story Analysis Using Natural Language Processing and Interactive Dashboards , 2020, J. Comput. Inf. Syst..

[209]  Thomas Lukasiewicz,et al.  A Surprisingly Robust Trick for the Winograd Schema Challenge , 2019, ACL.

[210]  Daniel A. Keim,et al.  Going beyond Visualization. Verbalization as Complementary Medium to Explain Machine Learning Models , 2018 .

[211]  John T. Stasko,et al.  Post-WIMP Interaction for Information Visualization , 2021, Found. Trends Hum. Comput. Interact..

[212]  David Murray-Rust,et al.  Design Patterns for Data Comics , 2018, CHI.

[213]  Haijun Xia Crosspower: Bridging Graphics and Linguistics , 2020, UIST.

[214]  Ryan A. Rossi,et al.  Personalized Visualization Recommendation , 2021, ACM Trans. Web.

[215]  Cagatay Turkay,et al.  Words of Estimative Correlation: Studying Verbalizations of Scatterplots , 2020, IEEE transactions on visualization and computer graphics.

[216]  Erik Marchi,et al.  Multi-Task Learning for Speaker Verification and Voice Trigger Detection , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[217]  Yoshua Bengio,et al.  FigureQA: An Annotated Figure Dataset for Visual Reasoning , 2017, ICLR.

[218]  R. Socher,et al.  Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning , 2017, ArXiv.

[219]  Robert S. Laramee,et al.  Storytelling and Visualization: An Extended Survey , 2018, Inf..

[220]  Loren G. Terveen,et al.  Understanding How People Use Natural Language to Ask for Recommendations , 2017, RecSys.

[221]  Johanna D. Moore,et al.  AutoBrief: a multimedia presentation system for assisting data analysis , 1997, Comput. Stand. Interfaces.

[222]  Yun Wang,et al.  DataShot: Automatic Generation of Fact Sheets from Tabular Data , 2020, IEEE Transactions on Visualization and Computer Graphics.

[223]  Rebecca E. Grinter,et al.  A Multi-Modal Natural Language Interface to an Information Visualization Environment , 2001, Int. J. Speech Technol..

[224]  Vidya Setlur,et al.  Snowy: Recommending Utterances for Conversational Visual Analysis , 2021, UIST.

[225]  Peter J. Haas,et al.  Foresight: Recommending Visual Insights , 2017, Proc. VLDB Endow..

[226]  Zhen-Hua Ling,et al.  Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge , 2016, 1611.04146.

[227]  Huamin Qu,et al.  InfoColorizer: Interactive Recommendation of Color Palettes for Infographics , 2021, IEEE Transactions on Visualization and Computer Graphics.

[228]  C. Lee Giles,et al.  Automatic Extraction of Data from Bar Charts , 2015, K-CAP.

[229]  Vidya Setlur,et al.  Applying Pragmatics Principles for Interaction with Visual Analytics , 2018, IEEE Transactions on Visualization and Computer Graphics.

[230]  Kanit Wongsuphasawat,et al.  Towards a general-purpose query language for visualization recommendation , 2016, HILDA '16.

[231]  Xi Chen,et al.  InfoNice: Easy Creation of Information Graphics , 2018, CHI.

[232]  Percy Liang,et al.  Compositional Semantic Parsing on Semi-Structured Tables , 2015, ACL.

[233]  Benny P. L. Lo,et al.  Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey , 2021, IEEE Reviews in Biomedical Engineering.

[234]  Benjamin Bach,et al.  Cheat Sheets for Data Visualization Techniques , 2020, CHI.

[235]  Ian Horrocks,et al.  ColNet: Embedding the Semantics of Web Tables for Column Type Prediction , 2018, AAAI.

[236]  Alvin Cheung,et al.  Learning a Neural Semantic Parser from User Feedback , 2017, ACL.

[237]  Aditi P. Deshpande,et al.  Summarization of Graph Using Question Answer Approach , 2020 .

[238]  Tobias Isenberg,et al.  Collaborative Work in Augmented Reality: A Survey , 2020, IEEE Transactions on Visualization and Computer Graphics.

[239]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[240]  Karrie Karahalios,et al.  DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization , 2015, UIST.

[241]  Cho-Jui Hsieh,et al.  VisualBERT: A Simple and Performant Baseline for Vision and Language , 2019, ArXiv.

[242]  Rynson W. H. Lau,et al.  Content-aware generative modeling of graphic design layouts , 2019, ACM Trans. Graph..

[243]  Lifeng Zhu,et al.  Line Graph or Scatter Plot? Automatic Selection of Methods for Visualizing Trends in Time Series , 2018, IEEE Transactions on Visualization and Computer Graphics.

[244]  N Shashikala,et al.  Natural Language Question Answering , 2018 .

[245]  Michael Rohs,et al.  Talk to Me Intelligibly: Investigating An Answer Space to Match the User's Language in Visual Analysis , 2019, Conference on Designing Interactive Systems.

[246]  Zhiyuan Liu,et al.  CPM-2: Large-scale Cost-effective Pre-trained Language Models , 2021, AI Open.

[247]  Sören Auer,et al.  SINA: Semantic interpretation of user queries for question answering on interlinked data , 2015, J. Web Semant..

[248]  Hang Li,et al.  “ Tony ” DNN Embedding for “ Tony ” Selective Read for “ Tony ” ( a ) Attention-based Encoder-Decoder ( RNNSearch ) ( c ) State Update s 4 SourceVocabulary Softmax Prob , 2016 .

[249]  Jeffrey Heer,et al.  Profiler: integrated statistical analysis and visualization for data quality assessment , 2012, AVI.

[250]  Luke S. Zettlemoyer,et al.  Deep Contextualized Word Representations , 2018, NAACL.

[251]  Shinji Nakadai,et al.  Meimei: An Efficient Probabilistic Approach for Semantically Annotating Tables , 2019, AAAI.

[252]  Bongshin Lee,et al.  Data@Hand: Fostering Visual Exploration of Personal Data on Smartphones Leveraging Speech and Touch Interaction , 2021, CHI.

[253]  W. Tan,et al.  Sato , 2019, Proc. VLDB Endow..

[254]  Furu Wei,et al.  VL-BERT: Pre-training of Generic Visual-Linguistic Representations , 2019, ICLR.

[255]  Jun Zhao,et al.  CogNet: Bridging Linguistic Knowledge, World Knowledge and Commonsense Knowledge , 2021, AAAI.

[256]  Robert S. Laramee,et al.  Survey of Surveys (SoS) ‐ Mapping The Landscape of Survey Papers in Information Visualization , 2017, Comput. Graph. Forum.

[257]  Guoliang Li,et al.  Making data visualization more efficient and effective: a survey , 2019, The VLDB Journal.

[258]  Jeffrey Heer,et al.  Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco , 2018, IEEE Transactions on Visualization and Computer Graphics.

[259]  James Miller,et al.  A comprehensive review of tools for exploratory analysis of tabular industrial datasets , 2018, Vis. Informatics.

[260]  Luke S. Zettlemoyer,et al.  End-to-end Neural Coreference Resolution , 2017, EMNLP.

[261]  Sanjay Silakari,et al.  Natural language Interface for Database: A Brief review , 2011 .

[262]  Huamin Qu,et al.  Augmenting Static Visualizations with PapARVis Designer , 2020, CHI.

[263]  Mira Dontcheva,et al.  Discovering natural language commands in multimodal interfaces , 2019, IUI.

[264]  Wei Chen,et al.  ScatterNet: A Deep Subjective Similarity Model for Visual Analysis of Scatterplots , 2020, IEEE Transactions on Visualization and Computer Graphics.

[265]  Scott Barlowe,et al.  Touch2Annotate: generating better annotations with less human effort on multi-touch interfaces , 2010, CHI Extended Abstracts.

[266]  Yoshihiko Suhara,et al.  Annotating Columns with Pre-trained Language Models , 2021, ArXiv.

[267]  Marti Hearst,et al.  Would You Like A Chart With That? Incorporating Visualizations into Conversational Interfaces , 2019, 2019 IEEE Visualization Conference (VIS).

[268]  Tamara Munzner,et al.  TimeLineCurator: Interactive Authoring of Visual Timelines from Unstructured Text , 2016, IEEE Transactions on Visualization and Computer Graphics.

[269]  Natalie Kerracher,et al.  Constructing and Evaluating Visualisation Task Classifications: Process and Considerations , 2017, Comput. Graph. Forum.

[270]  Arvind Satyanarayan,et al.  Vega-Lite: A Grammar of Interactive Graphics , 2018, IEEE Transactions on Visualization and Computer Graphics.

[271]  Younghoon Kim,et al.  Assessing Effects of Task and Data Distribution on the Effectiveness of Visual Encodings , 2018, Comput. Graph. Forum.

[272]  Cristina Conati,et al.  Gaze-Driven Adaptive Interventions for Magazine-Style Narrative Visualizations , 2019, IEEE transactions on visualization and computer graphics.

[273]  Nicholas Diakopoulos,et al.  Contextifier: automatic generation of annotated stock visualizations , 2013, CHI.

[274]  Kim Marriott,et al.  "Hey Model!" – Natural User Interactions and Agency in Accessible Interactive 3D Models , 2020, CHI.

[275]  Georgia Koutrika,et al.  Précis: from unstructured keywords as queries to structured databases as answers , 2007, The VLDB Journal.

[276]  Bongshin Lee,et al.  InChorus: Designing Consistent Multimodal Interactions for Data Visualization on Tablet Devices , 2020, CHI.

[277]  Maryam Nafari,et al.  Augmenting Visualization with Natural Language Translation of Interaction: A Usability Study , 2013, Comput. Graph. Forum.

[278]  Mukund Sundararajan,et al.  Analyza: Exploring Data with Conversation , 2017, IUI.

[279]  Enya Shen,et al.  TaskVis: Task-oriented Visualization Recommendation , 2021, EuroVis.

[280]  Kevin Zeng Hu,et al.  DIVE: A Mixed-Initiative System Supporting Integrated Data Exploration Workflows , 2018, HILDA@SIGMOD.

[281]  Charles Perin,et al.  What is Interaction for Data Visualization? , 2020, IEEE Transactions on Visualization and Computer Graphics.

[282]  Peter Seipel,et al.  Speak to your Software Visualization—Exploring Component-Based Software Architectures in Augmented Reality with a Conversational Interface , 2019, 2019 Working Conference on Software Visualization (VISSOFT).