ChartDialogs: Plotting from Natural Language Instructions

Shao, Yutong; Nakashole, Ndapa

doi:10.18653/v1/2020.acl-main.328

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…Generalpurpose assistants for driving a visual analytics conversation are proposed by Fast et al (2018); Kassel and Rohs (2018). Assistants implementing instruction following as in plot manipulation or navigation scenarios process and execute commands in a visualization environment (Shao and Nakashole, 2020;. Multi-modal dialogue assistants combine natural language input in oral or written form with touch gestures (Srinivasan and Stasko, 2018;.…”

Section: Discover Taskmentioning

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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Section: Discover Taskmentioning

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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“…In addition to the novel functionality, we also build on ChartDialogs (Shao and Nakashole, 2020) to enable other functionality.…”

Section: Contributions and Demonstration Overviewmentioning

confidence: 99%

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

2021

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We present the first multi-task learning model-named PhoNLP-for joint Vietnamese part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing. Experiments on Vietnamese benchmark datasets show that PhoNLP produces state-of-the-art results, outperforming a single-task learning approach that fine-tunes the pre-trained Vietnamese language model PhoBERT (Nguyen and Nguyen, 2020) for each task independently. We publicly release PhoNLP as an open-source toolkit under the Apache License 2.0. Although we specify PhoNLP for Vietnamese, our PhoNLP training and evaluation command scripts in fact can directly work for other languages that have a pre-trained BERT-based language model and gold annotated corpora available for the three tasks of POS tagging, NER and dependency parsing. We hope that PhoNLP can serve as a strong baseline and useful toolkit for future NLP research and applications to not only Vietnamese but also the other languages. Our PhoNLP is available at https://github. com/VinAIResearch/PhoNLP.

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