{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nvbench-a-large-scale-synthesized-dataset-for","title":"nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task","arxiv_id":"2112.12926","date":"2021-12-24","proceeding":null,"authors":["Yuyu Luo","Jiawei Tang","Guoliang Li"],"abstract":"NL2VIS - which translates natural language (NL) queries to corresponding visualizations (VIS) - has attracted more and more attention both in commercial visualization vendors and academic researchers. In the last few years, the advanced deep learning-based models have achieved human-like abilities in many natural language processing (NLP) tasks, which clearly tells us that the deep learning-based technique is a good choice to push the field of NL2VIS. However, a big balk is the lack of benchmarks with lots of (NL, VIS) pairs. We present nvBench, the first large-scale NL2VIS benchmark, containing 25,750 (NL, VIS) pairs from 750 tables over 105 domains, synthesized from (NL, SQL) benchmarks to support cross-domain NL2VIS task. The quality of nvBench has been extensively validated by 23 experts and 300+ crowd workers. Deep learning-based models training using nvBench demonstrate that nvBench can push the field of NL2VIS.","url_abs":"https://arxiv.org/abs/2112.12926v1","url_pdf":"https://arxiv.org/pdf/2112.12926v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[{"slug":"nvbench","name":"nvBench","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2112.12926","atlas_url":"https://app.syntology.ai/?focus=2112.12926","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}