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% increase in accuracy on the
proposed nvBench-Rob dataset.

研究针对文本到可视化的模型在语义匹配上对输入变化的鲁棒性进行全面分析，发现现有方法对于新的数据集变化，特别是在词汇和短语层面上，表现出不足的鲁棒性。为此，提出了一个新颖的基于 RAG 技术的框架 GRED，通过三个部分解决了自然语言变体、编程风格差异和数据模式变体等挑战，实验证明该模型在鲁棒性方面比目前领先的文本到可视化模型 RGVisNet 有 32% 的精度提升。

面向文本到可视化翻译的鲁棒性对抗词汇和短语变异

Towards Robustness of Text-to-Visualization Translation against Lexical  and Phrasal Variability

With the advances in deep learning, tremendous progress has been made with
chit-chat dialogue systems and task-oriented dialogue systems. However, these
two systems are often tackled separately in current methods. To achieve more
natural interaction with humans, a dialogue agent needs to be capable of both
chatting and accomplishing tasks. To this end, we propose a unified dialogue
system (UniDS) with the two aforementioned skills. In particular, we design a
unified dialogue data schema, compatible for both chit-chat and task-oriented
dialogues, and we train UniDS with mixed dialogue data from a pretrained
chit-chat dialogue model. Without adding extra parameters to SOTA baselines,
UniDS can alternatively handle chit-chat and task-oriented dialogues in a
unified framework. Experimental results demonstrate that the proposed UniDS
works comparably well as the pure chit-chat system, and it outperforms
state-of-the-art task-oriented dialogue systems. More importantly, UniDS
achieves better robustness as it is able to smoothly switch between two types
of dialogues. These results demonstrate the feasibility and potential of
building an one-for-all dialogue system.

提出了一种融合了聊天和任务对话技能的统一对话系统 (UniDS)，采用一个统一的对话数据模式，并通过对已有的聊天对话模型进行微调来训练 UniDS。实验结果表明，该系统在处理聊天和任务对话时与现有的最佳系统相当，同时在稳健性方面表现更好。