Page 369 - 《软件学报》2026年第2期

848 软件学报 2026 年第 37 卷第 2 期

[17] Shi TZ, Tatwawadi K, Chakrabarti K, Mao Y, Polozov O, Chen WZ. IncSQL: Training incremental text-to-SQL parsers with non-
deterministic oracles. arXiv:1809.05054, 2018.
[18] Yu T, Li ZF, Zhang ZL, Zhang R, Radev D. TypeSQL: Knowledge-based type-aware neural text-to-SQL generation. In: Proc. of the
2018 Conf. of the North American Chapter of the Association for Computational Linguistics. New Orleans: Association for
Computational Linguistics, 2018. 588–594. [doi: 10.18653/v1/N18-2093]
[19] Bogin B, Gardner M, Berant J. Representing schema structure with graph neural networks for text-to-SQL parsing. In: Proc. of the 57th
Annual Meeting of the Association for Computational Linguistics. Florence: Association for Computational Linguistics, 2019.
4560–4565. [doi: 10.18653/v1/P19-1448]
[20] Bogin B, Gardner M, Berant J. Global reasoning over database structures for text-to-SQL parsing. 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. 3659–3664. [doi: 10.18653/v1/D19-1378]
[21] Wang BL, Shin R, Liu XD, Polozov O, Richardson M. RAT-SQL: Relation-aware schema encoding and linking for text-to-SQL parsers.
In: Proc. of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics,
2020. 7567–7578. [doi: 10.18653/v1/2020.acl-main.677]
[22] Cao RS, Chen L, Chen Z, Zhao YB, Zhu S, Yu K. LGESQL: Line graph enhanced text-to-SQL model with mixed local and non-local
relations. In: Proc. of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th Int’l Joint Conf. on
Natural Language Processing. Association for Computational Linguistics, 2021. 2541–2555. [doi: 10.18653/v1/2021.acl-long.198]
[23] Cai RC, Yuan JJ, Xu BY, Hao ZF. SADGA: Structure-aware dual graph aggregation network for text-to-SQL. In: Proc. of the 35th Int’l
Conf. on Neural Information Processing Systems. Curran Associates Inc., 2021. 587.
2
[24] Hui BY, Geng RY, Wang LH, Qin BW, Li YY, Li BW, Sun J, Li YB. S SQL: Injecting syntax to question-schema interaction graph
encoder for text-to-SQL parsers. In: Proc. of the 2022 Findings of the Association for Computational Linguistics. Dublin: Association
for Computational Linguistics, 2022. 1254–1262. [doi: 10.18653/v1/2022.findings-acl.99]
[25] Chen Z, Chen L, Zhao YB, Cao RS, Xu ZH, Zhu S, Yu K. ShadowGNN: Graph projection neural network for text-to-SQL parser.
arXiv:2104.04689, 2021.
[26] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. In: Proc. of the
31st Int’l Conf. on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017. 6000–6010.
[27] He PC, Mao Y, Chakrabarti K, Chen WZ. X-SQL: Reinforce schema representation with context. arXiv:1908.08113, 2019.
[28] Hwang W, Yim J, Park S, Seo M. A comprehensive exploration on WikiSQL with table-aware word contextualization. arXiv:
1902.01069, 2019.
[29] Xie TB, Wu CH, Shi P, Zhong RQ, Scholak T, Yasunaga M, Wu CS, Zhong M, Yin PC, Wang SI, Zhong V, Wang BL, Li CZ, Boyle C,
Ni AS, Yao ZY, Radev D, Xiong CM, Kong LP, Zhang R, Smith NA, Zettlemoyer L, Yu T. UnifiedSKG: Unifying and multi-tasking
structured knowledge grounding with text-to-text language models. In: Proc. of the 2022 Conf. on Empirical Methods in Natural
Language Processing. Abu Dhabi: Association for Computational Linguistics, 2022. 602–631. [doi: 10.18653/v1/2022.emnlp-main.39]
[30] Scholak T, Li R, Bahdanau D, de Vries H, Pal C. DuoRAT: Towards simpler text-to-SQL models. In: Proc. of the 2021 Conf. of the
North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for
Computational Linguistics, 2021. 1313–1321. [doi: 10.18653/v1/2021.naacl-main.103]
[31] Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional Transformers for language understanding. In:
Proc. of the 2019 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language
Technologies, Vol. 1 (Long and Short Papers). Minneapolis: Association for Computational Linguistics, 2019. 4171–4186. [doi: 10.
18653/v1/N19-1423]
[32] Brown TB, Mann B, Ryder N, et al. Language models are few-shot learners. In: Proc. of the 34th Int’l Conf. on Neural Information
Processing Systems. Vancouver: Curran Associates Inc., 2020. 159.
[33] Choi D, Shin MC, Kim E, Shin DR. RYANSQL: Recursively applying sketch-based slot fillings for complex text-to-SQL in cross-
domain databases. Computational Linguistics, 2021, 47(2): 309–332. [doi: 10.1162/coli_a_00403]
[34] Guo JQ, Zhan ZC, Gao Y, Xiao Y, Lou JG, Liu T, Zhang DM. Towards complex text-to-SQL in cross-domain database with
intermediate representation. In: Proc. of the 57th Annual Meeting of the Association for Computational Linguistics. Florence:
Association for Computational Linguistics, 2019. 4524–4535. [doi: 10.18653/v1/P19-1444]
[35] Lyu Q, Chakrabarti K, Hathi S, Kundu S, Zhang JW, Chen Z. Hybrid ranking network for text-to-SQL. arXiv:2008.04759, 2020.
[36] Liu Q, Yang DJ, Zhang JH, Guo JQ, Zhou B, Lou JG. Awakening latent grounding from pretrained language models for semantic
parsing. In: Proc. of the 2021 Findings of the Association for Computational Linguistics. Association for Computational Linguistics,