25 7 REFERENCES [1] V. Zhong, C. Xiong, and R. Socher, ‘Seq2sql: Generating structured queries from natural language using reinforcement learning’, arXiv preprint arXiv:1709. 00103, 2017. [2] C. Finegan-Dollak et al., ‘Improving text-to-sql evaluation methodology’, arXiv preprint arXiv:1806. 09029, 2018. [3] T. Yu et al., ‘Sparc: Cross-domain semantic parsing in context’, arXiv preprint arXiv:1906. 02285, 2019. [4] C. Wang et al., ‘Robust text-to-sql generation with execution-guided decoding’, arXiv preprint arXiv:1807. 03100, 2018. [5] J. Guo et al., ‘Towards complex text-to-sql in cross-domain database with intermediate representation’, arXiv preprint arXiv:1905. 08205, 2019. [6] K. Xu, Y. Wang, Y. Wang, Z. Wen, and Y. Dong, ‘Sead: End-to-end text-to- sql generation with schema-aware denoising’, arXiv preprint arXiv:2105. 07911, 2021. [7] T. Guo and H. Gao, ‘Content enhanced bert-based text-to-sql generation’, arXiv preprint arXiv:1910. 07179, 2019. [8] Y. Cai and X. Wan, ‘IGSQL: Database schema interaction graph based neural model for context-dependent text-to-SQL generation’, arXiv preprint arXiv:2011. 05744, 2020. [9] B. Hui et al., ‘Improving text-to-sql with schema dependency learning’, arXiv preprint arXiv:2103. 04399, 2021. [10] B. Wang, R. Shin, X. Liu, O. Polozov, and M. Richardson, ‘Rat-sql: Relation- aware schema encoding and linking for text-to-sql parsers’, arXiv preprint arXiv:1911. 04942, 2019. [11] Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018a. Deep contextualized word representations. In NAACL. [12] W. Hwang, J. Yim, S. Park, and M. Seo, ‘A comprehensive exploration on wikisql with table-aware word contextualization’, arXiv preprint arXiv:1902. 01069, 2019. [13] R. Zhang et al., ‘Editing-based SQL query generation for cross-domain context-dependent questions’, arXiv preprint arXiv:1909. 00786, 2019. [14] K. Balaraman, ‘A Robust Text-to-SQL Parser With Optimized Pretraining Approach’, Dublin, National College of Ireland, 2021. [15] Hristidis,V.,Gravano, L., Papakonstantinou,Y.: Efficient IR-style keyword search over relational databases. In: VLDB, pp. 850–861 (2003). [16] Hristidis, V., Papakonstantinou, Y.: Discover: keyword search in relational databases. In: VLDB, pp. 670–681 (2002). [17] Y. Luo, X. Lin, W. Wang, and X. Zhou, ‘Spark: top-k keyword query in relational databases’, in Proceedings of the 2007 ACM SIGMOD international conference on management of data, 2007, pp. 115–126. [18] Z. Zhong, L. Mong Li, and L. Tok Wang, ‘Answering Keyword Queries 26 involving Aggregates and Group-Bys in Relational Databases’, 2015. [19] U. Brunner and K. Stockinger, ‘‘ValueNet: A neural text-to-SQL architecture incorporating values’, Proc. VLDB Endowment, pp. 1–14, 2020. [20] J. Pennington, R. Socher, and C. D. Manning, ‘Glove: Global vectors for word representation’, in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014, pp. 1532–1543. [21] J. D. M.-W. C. Kenton and L. K. Toutanova, ‘Bert: Pre-training of deep bidirectional transformers for language understanding’, in Proceedings of naacL-HLT, 2019, vol. 1, p. 2. [22] P. Yin, G. Neubig, W.-T. Yih, and S. Riedel, ‘TaBERT: Pretraining for joint understanding of textual and tabular data’, arXiv preprint arXiv:2005. 08314, 2020. [23] X. Xu, C. Liu, and D. Song, ‘Sqlnet: Generating structured queries from natural language without reinforcement learning’, arXiv preprint arXiv:1711. 04436, 2017. [24] D. Choi, M. C. Shin, E. Kim, and D. R. Shin, ‘Ryansql: Recursively applying sketch-based slot fillings for complex text-to-sql in cross-domain databases’, Computational Linguistics, vol. 47, no. 2, pp. 309–332, 2021. [25] L. Dong and M. Lapata, ‘Coarse-to-fine decoding for neural semantic parsing’, arXiv preprint arXiv:1805. 04793, 2018. [26] T. Scholak, N. Schucher, and D. Bahdanau, ‘PICARD: Parsing incrementally for constrained auto-regressive decoding from language models’, arXiv preprint arXiv:2109. 05093, 2021. [27] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: a robustly optimized bert pretraining approach (2019). [28] T. Yu et al., ‘Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task’, arXiv preprint arXiv:1809. 08887, 2018. [29] M. Lewis et al., ‘Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension’, arXiv preprint arXiv:1910. 13461, 2019. [30] R. Cao, L. Chen, Z. Chen, Y. Zhao, S. Zhu, and K. Yu, ‘LGESQL: line graph enhanced text-to-SQL model with mixed local and non-local relations’, arXiv preprint arXiv:2106. 01093, 2021. [31] T. Yu et al., ‘Grappa: Grammar-augmented pre-training for table semantic parsing’, arXiv preprint arXiv:2009. 13845, 2020. [32] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, ‘Focal Loss for dense object detection’, IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 2, pp. 318–327, Feb. 2020. [33] J. Qi et al., ‘RASAT: Integrating relational structures into pretrained Seq2Seq model for text-to-SQL’, arXiv [cs.CL], 14-May-2022. [34] C. Raffel et al., ‘Exploring the limits of transfer learning with a unified text-to- text transformer’, arXiv [cs.LG], 23-Oct-2019. 27 [35] Y. Gan et al., ‘Towards robustness of text-to-SQL models against synonym substitution’, arXiv [cs.CL], 02-Jun-2021. [36] “Models”, Hugging Face. [Online]. Available: https://huggingface.co/models. [Accessed: 09- May- 2023] [37] I. Loshchilov and F. Hutter, ‘Decoupled weight decay regularization’, arXiv preprint arXiv:1711. 05101, 2017. [38] Aadhil Rushdy, "Google Colaboratory", Colab.research.google.com, 2023. [Online]. Available: https://colab.research.google.com/drive/1WLbHPXFWzw_K14fwoXqqKCm3 rt3KgVcY?usp=sharing. [Accessed: 22- May- 2023] [39] T. Yu et al., ‘CoSQL: A conversational text-to-SQL challenge towards cross- domain natural language interfaces to databases’, arXiv preprint arXiv:1909. 05378, 2019. [40] D. H. D. Warren and F. C. N. Pereira, ‘An efficient easily adaptable system for interpreting natural language queries’, American journal of computational linguistics, vol. 8, no. 3–4, pp. 110–122, 1982. [41] I. Androutsopoulos, G. D. Ritchie, and P. Thanisch, ‘Natural language interfaces to databases–an introduction’, Natural language engineering, vol. 1, no. 1, pp. 29–81, 1995. [42] A.-M. Popescu, A. Armanasu, O. Etzioni, D. Ko, and A. Yates, ‘Modern natural language interfaces to databases: Composing statistical parsing with semantic tractability’, in COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics, 2004, pp. 141–147. [43] H. Bast and E. Haussmann, ‘More accurate question answering on freebase’, in Proceedings of the 24th ACM international on conference on information and knowledge management, 2015, pp. 1431–1440. [44] L. Blunschi, C. Jossen, D. Kossman, M. Mori, and K. Stockinger, ‘Soda: Generating sql for business users’, arXiv preprint arXiv:1207. 0134, 2012. [45] A. Simitsis, G. Koutrika, and Y. Ioannidis, ‘Precis: from unstructured ‘keywords as queries to structured databases as answers’, The VLDB Journal, vol. 17, pp. 117–149, 2008. [46] D. Damljanovic, M. Agatonovic, and H. Cunningham, ‘Natural language interfaces to ontologies: Combining syntactic analysis and ontologybased lookup through the user interaction’, in The Semantic Web: Research and Applications: 7th Extended Semantic Web Conference, ESWC 2010, Heraklion, Crete, Greece, May 30–June 3, 2010, Proceedings, Part I 7, 2010, pp. 106–120. [47] W. Zheng, H. Cheng, L. Zou, J. X. Yu, and K. Zhao, ‘Natural language question/answering: Let users talk with the knowledge graph’, in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017, pp. 217–226. [48] A. Vaswani et al., ‘Attention is all you need’, Advances in neural information processing systems, vol. 30, 2017.