UniSAr: a unified structure-aware autoregressive language model for text-to-SQL semantic parsing
Author:
Funder
Grant
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s13042-023-01898-3.pdf
Reference45 articles.
1. Zhong V, Xiong C, Socher R (2017) Seq2sql: generating structured queries from natural language using reinforcement learning. arXiv:1709.00103
2. Yu T, Zhang R, Yang K, Yasunaga M, Wang D, Li Z, Ma J, Li I, Yao Q, Roman S, Zhang Z, Radev D (2018) Spider: a large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task. EMNLP2018. https://aclanthology.org/D18-1425
3. Guo J, Zhan Z, Gao Y, Xiao Y, Lou J-G, Liu T, Zhang D (2019) Towards complex text-to-SQL in cross-domain database with intermediate representation. Association for Computational Linguistics, Florence, Italy. https://aclanthology.org/P19-1444. Accessed 2 Aug 2019
4. Yu T, Zhang R, Yasunaga M, Tan YC, Lin XV, Li S, Er H, Li I, Pang B, Chen T, Ji E, Dixit S, Proctor D, Shim S, Kraft J, Zhang V, Xiong C, Socher R, Radev D (2019) SParC: cross-domain semantic parsing in context. Association for Computational Linguistics, Florence, Italy. https://aclanthology.org/P19-1443. Accessed 2 Aug 2019
5. Sun N, Yang X, Liu Y (2020) TableQA: a large-scale Chinese text-to-SQL dataset for table-aware SQL generation. arXiv:2006.06434
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