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姬涛 等: AI 赋能的关系型数据库系统研究: 标准化、技术与挑战                                                849


                      2021. 1174–1189. [doi: 10.18653/v1/2021.findings-acl.100]
                 [37]   Yin PC, Neubig G, Yih WT, Riedel S. TaBERT: Pretraining for joint understanding of textual and tabular data. In: Proc. of the 58th
                      Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2020. 8413–8426. [doi:
                      10.18653/v1/2020.acl-main.745]
                 [38]   Yu T, Wu CS, Lin XV, Wang BL, Tan YC, Yang XY, Radev DR, Socher R, Xiong CM. GraPPa: Grammar-augmented pre-training for
                      table semantic parsing. In: Proc. of the 9th Int’l Conf. on Learning Representations. OpenReview.net, 2021.
                 [39]   Shi P, Ng P, Wang ZG, Zhu HH, Li AH, Wang J, Nogueira dos Santos C, Xiang B. Learning contextual representations for semantic
                      parsing with generation-augmented pre-training. In: Proc. of the 35th AAAI Conf. on Artificial Intelligence. AAAI, 2020. 13806–13814.
                      [doi: 10.1609/aaai.v35i15.17627]
                 [40]   Yan Z, Tang DY, Duan N, Liu SJ, Wang WD, Jiang DX, Zhou M, Li ZJ. Assertion-based QA with question-aware open information
                      extraction. In: Proc. of the 32nd AAAI Conf. on Artificial Intelligence. New Orleans: AAAI, 2018. 6021–6028. [doi: 10.1609/aaai.v32i1.
                      12052]
                 [41]   Yu T, Yasunaga M, Yang K, Zhang R, Wang DX, Li ZF, Radev D. SyntaxSQLNet: Syntax tree networks for complex and cross-domain
                      text-to-SQL  task.  In:  Proc.  of  the  2018  Conf.  on  Empirical  Methods  in  Natural  Language  Processing.  Brussels:  Association  for
                      Computational Linguistics, 2018. 1653–1663. [doi: 10.18653/v1/D18-1193]
                 [42]   Rubin O, Berant J. SmBoP: Semi-autoregressive bottom-up semantic parsing. In: Proc. of the 2021 Conf. of the North American Chapter
                      of the Association for Computational Linguistics. Association for Computational Linguistics, 2021. 311–324. [doi: 10.18653/v1/2021.
                      naacl-main.29]
                 [43]   Dong L, Lapata M. Coarse-to-fine decoding for neural semantic parsing. In: Proc. of the 56th Annual Meeting of the Association for
                      Computational Linguistics. Melbourne: Association for Computational Linguistics, 2018. 731–742. [doi: 10.18653/v1/P18-1068]
                 [44]   Guo HL, Gao T, Wang F, Ma C. Bidirectional attention for SQL generation. In: Proc. of the 4th IEEE Int’l Conf. on Cloud Computing
                      and Big Data Analysis. Chengdu: IEEE, 2019. 676–682. [doi: 10.1109/ICCCBDA.2019.8725626]
                 [45]   Wang BL, Titov I, Lapata M. Learning semantic parsers from denotations with latent structured alignments and abstract programs. In:
                      Proc.  of  the  2019  Conf.  on  Empirical  Methods  in  Natural  Language  Processing  and  the  9th  Int’l  Joint  Conf.  on  Natural  Language
                      Processing. Hong Kong: Association for Computational Linguistics, 2019. 3774–3785. [doi: 10.18653/v1/D19-1391]
                 [46]   Brunner U, Stockinger K. ValueNet: A natural language-to-SQL system that learns from database information. In: Proc. of the 37th
                      IEEE Int’l Conf. on Data Engineering. Chania: IEEE, 2021. 2177–2182. [doi: 10.1109/ICDE51399.2021.00220]
                 [47]   Gan YJ, Chen XY, Xie JX, Purver M, Woodward JR, Drake J, Zhang QF. Natural SQL: Making SQL easier to infer from natural
                      language specifications. arXiv:2109.05153, 2021.
                 [48]   Suhr A, Chang MW, Shaw P, Lee K. Exploring unexplored generalization challenges for cross-database semantic parsing. In: Proc. of
                      the  58th  Annual  Meeting  of  the  Association  for  Computational  Linguistics.  Association  for  Computational  Linguistics,  2020.
                      8372–8388. [doi: 10.18653/v1/2020.acl-main.742]
                 [49]   Yu T, Zhang R, Yang K, Yasunaga M, Wang DX, Li ZF, Ma J, Li I, Yao QN, Roman S, Zhang ZL, Radev D. Spider: A large-scale
                      human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task. arXiv:1809.08887, 2019.
                 [50]   Liu AW, Hu XM, Wen LJ, Yu PS. A comprehensive evaluation of ChatGPT’s zero-shot text-to-SQL capability. arXiv:2303.13547,
                      2023.
                 [51]   Rajkumar N, Li R, Bahdanau D. Evaluating the text-to-SQL capabilities of large language models. arXiv:2204.00498, 2022.
                 [52]   Chang SC, Fosler-Lussier E. Selective demonstrations for cross-domain text-to-SQL. In: Proc. of the 2023 Findings of the Association
                      for  Computational  Linguistics.  Singapore:  Association  for  Computational  Linguistics,  2023.  14174–14189.  [doi: 10.18653/v1/2023.
                      findings-emnlp.944]
                 [53]   Pourreza M, Rafiei D. DTS-SQL: Decomposed text-to-SQL with small large language models. In: Proc. of the 2024 Findings of the
                      Association  for  Computational  Linguistics.  Miami:  Association  for  Computational  Linguistics,  2024.  8212–8220.  [doi: 10.18653/v1/
                      2024.findings-emnlp.481]
                 [54]   Pourreza M, Rafiei D. DIN-SQL: Decomposed in-context learning of text-to-SQL with self-correction. In: Proc. of the 37th Int’l Conf.
                      on Neural Information Processing Systems. New Orleans: Curran Associates Inc., 2023. 1577.
                 [55]   Li ZS, Wang X, Zhao JJ, Yang S, Du GQ, Hu XR, Zhang B, Ye YX, Li ZY, Zhao R, Mao HY. PET-SQL: A prompt-enhanced two-
                      round refinement of text-to-SQL with cross-consistency. arXiv:2403.09732, 2024.
                 [56]   Fan YK, He ZY, Ren TH, Huang C, Jing YN, Zhang K, Wang XS. Metasql: A generate-then-rank framework for natural language to
                      SQL  translation.  In:  Proc.  of  the  40th  IEEE  Int’l  Conf.  on  Data  Engineering.  Utrecht:  IEEE,  2024.  1765–1778.  [doi:  10.1109/
                      ICDE60146.2024.00143]
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