Kai Lawonn


2022

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The Why and The How: A Survey on Natural Language Interaction in Visualization
Henrik Voigt | Ozge Alacam | Monique Meuschke | Kai Lawonn | Sina Zarrieß
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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 state-of-the-art of how current natural language interfaces support their performance. We examine applied NLP methods and discuss human-machine 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.

2021

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Challenges in Designing Natural Language Interfaces for Complex Visual Models
Henrik Voigt | Monique Meuschke | Kai Lawonn | Sina Zarrieß
Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing

Intuitive interaction with visual models becomes an increasingly important task in the field of Visualization (VIS) and verbal interaction represents a significant aspect of it. Vice versa, modeling verbal interaction in visual environments is a major trend in ongoing research in NLP. To date, research on Language & Vision, however, mostly happens at the intersection of NLP and Computer Vision (CV), and much less at the intersection of NLP and Visualization, which is an important area in Human-Computer Interaction (HCI). This paper presents a brief survey of recent work on interactive tasks and set-ups in NLP and Visualization. We discuss the respective methods, show interesting gaps, and conclude by suggesting neural, visually grounded dialogue modeling as a promising potential for NLIs for visual models.