[1] 高良才,李一博,都林,等.表格识别技术研究进展[J]. 中国图象图形学报,2022,27(06): 1898-1917.
[2] 马浩,戴国琳,刘新遥,等.医学知识图谱自动构建研究[J]. 医学信息,2022,35(04): 10-12.
[3] YANG J, XIANG F, LI R, et al. Intelligent bridge management via big data knowledge engineering[J]. Automation in Construction, 2022, 135: 104118.
[4] 梁清源,朱琪豪,孙泽宇,等.基于深度学习的SQL生成研究综述[J].中国科学(信息科学),2022, 52(08): 1363-1392.
[5] 郑泳智,朱定局,吴惠粦,等.知识图谱问答领域综述[J].计算机系统应用,2022,31(04): 1-13.
[6] THERASA M, MATHIVANAN G. Survey of machine reading comprehension models and its evaluation metrics[C]//Proceedings of the 6th International Conference on Computing Methodologies and Communication, 2022: 1006-1013.
[7] ZHONG V, XIONG C, SOCHER R. Seq2sql: Generating structured queries from natural language using reinforcement learning[J]. arXiv preprint arXiv:1709.00103, 2017.
[8] 李智,王震,杨赋庚,等.基于表格的自动问答研究与展望[J].计算机工程与应用,2021,57(13): 67-76.
[9] ZHANG S, BALOG K. Web table extraction, retrieval and augmentation: A survey[J]. ACM Transactions on Intelligent Systems and Technology, 2020, 11(2): 1-35.
[10] DONG H, CHENG Z, HE X, et al. Table pretraining: A survey on model architectures, pretraining objectives, and downstream tasks[J]. arXiv preprint arXiv:2201.09745, 2022.
[11] QIN B, HUI B, WANG L, et al. A survey on text-to-sql parsing: Concepts, methods, and future directions[J]. arXiv preprint arXiv:2208.13629, 2022.
[12] PASUPAT P, LIANG P. Compositional semantic parsing on semi-structured tables[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2015: 1470-1480.
[13] PRICE P.Evaluation of spoken language systems: The atis domain[C]//Proceedings of the 3rd Darpa Speech & Natural Language Workshop,1990: 24-27.
[14] SUN N, YANG X, LIU Y. TableQA: A large-scale Chinese text-to-sql dataset for table-aware sql generation[J]. arXiv preprint arXiv:2006.06434, 2020.
[15] CHEN Z, CHEN W, SMILEY C, et al. FinQA: A dataset of numerical reasoning over financial data[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2021: 3697-3711.
[16] ZHU F, LEI W, HUANG Y, et al. TAT-QA: A question answering benchmark on a hybrid of tabular and textual content in finance[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021: 3277-3287.
[17] XU X, LIU C, SONG D. Sqlnet: Generating structured queries from natural language without reinforcement learning[J]. arXiv preprint arXiv:1711.04436, 2017.
[18] DONG L, LAPATA M. Coarse-to-fine decoding for neural semantic parsing[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2018: 731-742.
[19] YU T, LI Z, ZHANG Z, et al. TypeSQL: Knowledge-based type-aware neural text-to-sql generation[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, 2018: 588-594.
[20] YIN P, NEUBIG G, YIP W, et al. TaBERT: Pretraining for joint understanding of textual and tabular data[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 8413-8426.
[21] YU T, WU C, LIN X, et al. GraPPa: Grammar-augmented pre-training for table semantic parsing[C]//Proceedings of the International Conference on Learning Representations, 2020.
[22] XUAN K, WANG Y, WANG Y, et al. SeaD: End-to-end text-to-sql generation with schema-aware denoising[J]. arXiv preprint arXiv:2105.07911, 2021.
[23] LIU Q, CHEN B, GUO J, et al. TAPEX: Table pre-training via learning a neural sql executor[C]//Proceedings of the International Conference on Learning Representations, 2021: 1-16.
[24] HERZIG J, NOWAK P, MUELLER T, et al. TaPas: Weakly supervised table parsing via pre-training[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 4320-4333.
[25] WOODS W.The lunar sciences natural language information system[R].US: BBN Report, 1972: 1-388.
[26] DAS D, CHEN D, MARTINS A, et al. Frame-semantic parsing[J]. Computational Linguistics, 2014, 40(1): 9-56.
[27] POPESCU A, ETZIONI O, KAUTZ H. Towards a theory of natural language interfaces to databases[C]//Proceedings of the 8th International Conference on Intelligent User Interfaces, 2003.
[28] CHEN J, JIMéNEZ RUIZ E, HORROCKS I, et al. Colnet: Embedding the semantics of web tables for column type prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(01): 29-36.
[29] GOL M, PUJARA J, SZEKELY P. Tabular cell classification using pre-trained cell embeddings[C]//Proceedings of the IEEE International Conference on Data Mining. IEEE, 2019: 230-239.
[30] YU T, ZHANG R, YANG K, et al. Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2020: 3911-3921.
[31] YU T, YASUNAGA M, YANG K, et al. SyntaxSQLNet: Syntax tree networks for complex and cross-domain text-to-sql task[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2018: 1653-1663.
[32] BOGIN B, BERANT J, GARDNER M. Representing schema structure with graph neural networks for text-to-sql parsing[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 4560-4565.
[33] BOGIN B, GARDNER M, BERANT J. Global reasoning over database structures for text-to-sql parsing[J]. arXiv preprint arXiv:1908.11214, 2019.
[34] 曹合心,赵亮,李雪峰. 图神经网络在 Text-to-SQL 解析中的技术研究[J]. 计算机科学, 2022, 49(4): 110-115.
[35] WANG B, SHIN R, LIU X, et al. RAT-SQL: Relation-aware schema encoding and linking for text-to-sql parsers[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 7567-7578.
[36] GUO J, ZHAN Z, GAO Y, et al. Towards complex text-to-sql in cross-domain database with intermediate representation[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 4524-4535.
[37] SHI P, NG P, WANG Z, et al. Learning contextual representations for semantic parsing with generation-augmented pre-training[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(15): 13806-13814.
[38] QIN B, WANG L, HUI B, et al. SDCUP: Schema dependency-enhanced curriculum pre-training for table semantic parsing[J]. arXiv preprint arXiv:2111.09486, 2021.
[39] HUO S, MA T, CHEN J, et al. Graph enhanced cross-domain text-to-sql generation[C]//Proceedings of the 13th Workshop on Graph-Based Methods for Natural Language Processing, 2019: 159-163.
[40] YU T, ZHANG R, YASUNAGA M, et al. SParC: Cross-domain semantic parsing in context[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 4511-4523.
[41] IYYER M, YIH W, CHANG M. Search-based neural structured learning for sequential question answering[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017: 1821-1831.
[42] YU T, ZHANG R, ER H, et al. CoSQL: A conversational text-to-sql challenge towards cross-domain natural language interfaces to databases[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 1962-1979.
[43] GUO J, SI Z, WANG Y, et al. CHASE: A large-scale and pragmatic Chinese dataset for cross-database context-dependent text-to-sql[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021: 2316-2331.
[44] CHEN W, ZHA H, CHEN Z, et al. HybridQA: A dataset of multi-hop question answering over tabular and textual data[G]//Proceedings of the Association for Computational Linguistics, 2020: 1026-1036.
[45] ZHONG R, YU T, DAN K. Semantic evaluation for text-to-sql with distilled test suites[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2020: 396-411.
[46] MLLER T, PICCINNO F, NICOSIA M, et al. Answering conversational questions on structured data without logical forms[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019:5902-5910.
[47] LIU Q, CHEN B, GUO J, et al. How far are we from effective context modeling?: An exploratory study on semantic parsing in context[C]//Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence, 2021.
[48] ZHANG R, YU T, ER H, et al. Editing-based sql query generation for cross-domain context-dependent questions[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Association for Computational Linguistics, 2020: 5338-5349.
[49] ZHENG Y, WANG H, DONG B, et al. HIE-SQL: History information enhanced network for context-dependent text-to-sql semantic parsing[G]//Proceedings of the Association for Computational Linguistics, 2022: 2997-3007.
[50] XIAO D, CHAI L, ZHANG Q, et al. CQR-SQL: Conversational question reformulation enhanced context-dependent text-to-sql parsers[J]. arXiv preprint arXiv: 2205.07686, 2022.
[51] EISENSCHLOS J, KRICHENE S, MUELLER T. Understanding tables with intermediate pre-training[G]//Proceedings of the Association for Computational Linguistics, 2020: 281-296.
[52] YANG J, GUPTA A, UPADHYAY S, et al. TableFormer: Robust transformer modeling for table-text encoding[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022: 528-537.
[53] YU T, ZHANG R, POLOZOV A, et al. Score: Pre-training for context representation in conversational semantic parsing[C]//Proceedings of the International Conference on Learning Representations, 2020: 1-16.
[54] MISRA D, CHANG M, HE X, et al. Policy shaping and generalized update equations for semantic parsing from denotations[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2018: 2442-2452.
[55] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017.
[56] DEVLIN J, CHANG M, LEE K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv:1810.04805, 2018.
[57] RADFORD A, NARASIMHAN K, SALIMANS T, et al. Improving language understanding by generative pre-training[EB/OL]. https://cdn.openai.com/research-covers/Language-unsugervised/Langage-understandly-paper.pdf[2022-04-15]
[58] WANG C, TATWAWADI K, BROCKSCHMIDT M, et al. Robust text-to-sql generation with execution-guided decoding[J]. arXiv preprint arXiv:1807.03100, 2018.
[59] DENG X, HASSAN A, MEEK C, et al. Structure-Grounded Pretraining for Text-to-SQL[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021: 1337-1350.
[60] IIDA H, THAI D, MANJUNATHA V, et al. TABBIE: Pretrained representations of tabular data[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Lin-guistics: Human Language Technologies,2021: 3446-3456.
[61] CHEMMENGATH S, KUMAR V, BHARADWAJ S, et al. Topic transferable table question answering[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing,2021: 4159-4172.
[62] BROWN T, MANN B, RYDER N, et al. Language models are few-shot learners[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems, 2020, 33: 1877-1901.
[63] RAFFEL C, SHAZEER N, ROBERTS A, et al. Exploring the limits of transfer learning with a unified text-to-text transformer[J]. Journal of Machine Learning Research, 2020, 21: 1-67.
[64] CHEN W, SU Y, YAN X, et al. KGPT: KnowleDGE-grounded pre-training for data-to-text generation[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2020: 8635-8648.
[65] GONG H, SUN Y, FENG X, et al. Tablegpt: Few-shot table-to-text generation with table structure reconstruction and content matching[C]//Proceedings of the 28th International Conference on Computational Linguistics, 2020: 1978-1988.
[66] EISENSCHLOS J, GOR M, MUELLER T, et al. MATE: Multi-view attention for table transformer efficiency [C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2021: 7606-7619.
[67] DAI Z, YANG Z, YANG Y, et al. Transformer-XL: Attentive language models beyond a fixed-length context[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 2978-2988.
[68] AINSLIE J, ONTANON S, ALBERTI C, et al. ETC: Encoding long and structured inputs in transformers[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2020: 268-284.
[69] 吕剑清,王先兵,陈刚,等.面向工业生产的中文Text-to-SQL模型[J].计算机应用,2022:1-8.
[70] MIN Q, SHI Y, ZHANG Y. A pilot study for Chinese sql semantic parsing[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 3652-3658.
[71] WANG L, ZHANG A, WU K, et al. DuSQL: A large-scale and pragmatic Chinese text-to-sql dataset[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2020.