一句话：提出 NL2Viz，结合数据/程序上下文与软硬约束，用约束语法引导合成将自然语言转为可视化，并支持结果精化复用。
自然语言到代码（NL2CODE）让非专业用户可用自然语言生成数据可视化等程序，但面临用户词语的上下文语义、缺少代码生成所需细节以及生成结果不完美需进一步精化三大挑战。本文面向自然语言到可视化（NL2Viz）提出新方法及工具 NL2Viz，其特点包括：同时利用用户自然语言输入以及该查询所依赖的数据和程序上下文；使用硬约束和软约束区分从用户输入与数据/程序上下文中抽取约束的置信度；支持对生成结果的精化与复用。摘要未报告具体实验评估。
Recent development in NL2CODE (Natural Language to Code) research allows end-users, especially novice programmers to create a concrete implementation of their ideas such as data visualization by providing natural language (NL) instructions. An NL2CODE system often fails to achieve its goal due to three major challenges: the user's words have contextual semantics, the user may not include all details needed for code generation, and the system results are imperfect and require further refinement. To address the aforementioned three challenges for NL to Visualization, we propose a new approach and its supporting tool named NL2VIZ with three salient features: (1) leveraging not only the user's NL input but also the data and program context that the NL query is upon, (2) using hard/soft constraints to reflect different confidence levels in the constraints retrieved from the user input and data/program context, and (3) providing support for result refinement and reuse.
DOI 原文 ·
@inproceedings{paperbot7010,
title = {NL2Viz: natural language to visualization via constrained syntax-guided synthesis},
author = {Zhengkai Wu and Vu Le and Ashish Tiwari and Sumit Gulwani and Arjun Radhakrishna and Ivan Radiček and Gustavo Soares and Xinyu Wang and Zhenwen Li and Tao Xie},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3549140}
}