Towards Robustness of Text-to-Visualization Translation against Lexical and Phrasal Variability
arxiv(2024)
摘要
Text-to-Vis is an emerging task in the natural language processing (NLP) area
that aims to automatically generate data visualizations from natural language
questions (NLQs). Despite their progress, existing text-to-vis models often
heavily rely on lexical matching between words in the questions and tokens in
data schemas. This overreliance on lexical matching may lead to a diminished
level of model robustness against input variations. In this study, we
thoroughly examine the robustness of current text-to-vis models, an area that
has not previously been explored. In particular, we construct the first
robustness dataset nvBench-Rob, which contains diverse lexical and phrasal
variations based on the original text-to-vis benchmark nvBench. Then, we found
that the performance of existing text-to-vis models on this new dataset
dramatically drops, implying that these methods exhibit inadequate robustness
overall. Finally, we propose a novel framework based on Retrieval-Augmented
Generation (RAG) technique, named GRED, specifically designed to address input
perturbations in these two variants. The framework consists of three parts:
NLQ-Retrieval Generator, Visualization Query-Retrieval Retuner and
Annotation-based Debugger, which are used to tackle the challenges posed by
natural language variants, programming style differences and data schema
variants, respectively. Extensive experimental evaluations show that, compared
to the state-of-the-art model RGVisNet in the Text-to-Vis field, RGDR performs
better in terms of model robustness, with a 32
proposed nvBench-Rob dataset.
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