Papers by Henrik Voigt

5 papers
The Why and The How: A Survey on Natural Language Interaction in Visualization (2022.naacl-main)

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Challenge: Recent research shows that different forms of natural language-based interaction prove suitable to support users in accomplishing various visualization tasks.
Approach: They propose a taxonomy of visualization tasks and a classification system to illustrate the state-of-the-art of natural language-based interaction in visualization.
Outcome: The proposed model can support annotations, recommendations, explanations, and documentation tasks.
Paparazzi: A Deep Dive into the Capabilities of Language and Vision Models for Grounding Viewpoint Descriptions (2023.findings-eacl)

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Challenge: Existing language and vision models can be used for language understanding in 3D environments . however, existing models lack specific properties and biases that limit their performance .
Approach: They propose a framework that uses a camera to generate images from different viewpoints and evaluate them in terms of their similarity to natural language descriptions.
Outcome: The proposed model performs poorly on most canonical views and fine-tunes using hard negative sampling and random contrasting yields good results even under conditions with little available training data.
Plots Made Quickly: An Efficient Approach for Generating Visualizations from Natural Language Queries (2024.lrec-main)

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Challenge: Existing methods for generating visualizations from natural language queries have not fully incorporated state-of-the-art techniques such as pre-trained LMs.
Approach: They propose to generate a valid Vega-Lite specification from a data frame and a query as input and render it as a visualization.
Outcome: The proposed model scales better with pre-trained LMs than current state-of-the-art models on the NL2VIS benchmark nvBench.
VIST5: An Adaptive, Retrieval-Augmented Language Model for Visualization-oriented Dialog (2023.emnlp-demo)

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Challenge: a number of visualization-oriented dialog systems rely on tailored dialog agents to analyze domain-specific data and operate domain- specific visualization tools and libraries.
Approach: They propose a visualization-oriented dialog system that focuses on easy adaptability to an application domain and easy transferability of language-controllable visualization library functions between applications.
Outcome: The proposed system is based on a retrieval-augmented T5 language model that leverages few-shot learning capabilities to enable a rapid adaptation of the system.
KeywordScape: Visual Document Exploration using Contextualized Keyword Embeddings (2022.emnlp-demos)

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Challenge: Existing tools for document visualization assume that keywords have static meanings, but contextualized word embeddings are unrealistic.
Approach: They propose a visual exploration tool that visualizes contextualized word embeddings in documents based on keywords.
Outcome: The proposed tool visualizes keywords in terms of their contextualized embeddings in a semantic landscape that keeps keywords with similar context close to each other, allowing for a more precise search and comparison of documents.

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