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Sevi: Speech-to-Visualization through Neural Machine Translation

Summary: Sevi is an end-to-end Speech-to-Visualization system mapping natural language or speech to visualizations via ncNet, a neural MT model trained on nvBench. It couples Speech2Text with Text2VIS to enable cross-domain visualization synthesis, demonstrated on COVID-19 and NBA datasets across nvBench’s 105 domains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6428
Venue
SIGMOD
Year
2022
Pagerank
5.1845938e-05
Overall Rank
9,976 | 31.56%
DOI
10.1145/3514221.3520150

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{tang_sigmod22,
        title = {{Sevi: Speech-to-Visualization through Neural Machine Translation}},
        author = {Tang, Jiawei and Luo, Yuyu and Ouzzani, Mourad and Li, Guoliang and Chen, Hongyang},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3520150},
        url = {https://dl.acm.org/doi/10.1145/3514221.3520150},
        year = {2022}
}

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