1. Kamath, A. A Survey on Semantic Parsing / A. Kamath, R. Das // arXiv. - 2018. - 1812.00978. DOI: 10.48550/arXiv.1812.00978
2. Xu, X. SQLnet: Generating Structured Queries from Natural Language without Reinforcement Learning / X. Xu, C. Liu, D. Song // arXiv. - 2017. - 1711.04436. DOI: 10.48550/arXiv.1711.04436
3. A Comprehensive Exploration on wikiSQL with Table-aware Word Contextualization / W. Hwang, J. Yim, S. Park, M. Seo // arXiv. - 2019. - 1902.01069. 10.48550/ arXiv.1902.01069. DOI: 10.48550/arXiv.1902.01069
4. RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases / D.H. Choi, M. Ch. Shin, E.G. Kim, D. R. Shin // Computational Linguistics. - 2021. - Vol. 47, № 2. - P. 309-332. DOI: 10.1162/coli_a_00403
5. Bogin, B. Global Reasoning over Database Structures for Text to-SQL Parsing / B. Bogin, M. Gardner, J. Berant // Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLPIJCNLP), 3-7 November 2019, Hong Kong, China. - Association for Computational Linguistics, 2019. - P. 3659-3664. DOI: 10.18653/v1/D19-1378
6. LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations / R. Cao, L. Chen, Z. Chen, Y. Zhao, S. Zhu, K. Yu // Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 1-6 August 2021, Online. - Association for Computational Linguistics, 2021. - P. 2541-2555. DOI: 10.18653/v1/2021.acl-long.198
7. SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL / R. Cai, J. Yuan, B.Xu, Z. Hao // arXiv. - 2021. - 2111.00653. DOI: 10.48550/arXiv.2111.00653
8. S2SQL: Injecting syntax to question-schema interaction graph encoder for text-to-SQL parsers / B. Hui, R. Geng, L. Wang, B. Qin, B. Li, J. Sun, Y. Li // arXiv. - 2022. - 2408.03256. DOI: 10.48550/arXiv.2408.03256
9. Relational Graph Attention Network for Aspect-based Sentiment Analysis / K. Wang, W. Shen, Y. Yang, X. Quan, R. Wang // Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5-10 July 2020, Online. - Association for Computational Linguistics, 2020. - P. 3229-3238. DOI: 10.18653/v1/2020.acl-main.295
10. Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both? / P. Shaw, M.-W. Chang, P. Pasupat, K. Toutanova // Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 1-6 August 2021, Online - Association for Computational Linguistics, 2021. - P. 922-938. 10.18653/v1/ 2021.acl-long.75. DOI: 10.18653/v1/2021.acl-long.75
11. Scholak, T. PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models / T. Scholak, N. Schucher, D. Bahdanau // Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, 7-11 November 2011. - Association for Computational Linguistics, 2021. - P. 9895-9901. DOI: 10.18653/v1/2021.emnlp-main.779
12. Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task / T. Yu, R. Zhang, K. Yang [et al.] // Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium, 31 October - 4 November 2018. - Association for Computational Linguistics, 2018. - P. 3911-3921. DOI: 10.18653/v1/D18-1425
13. Shaw, P. Self-Attention with Relative Position Representations / P. Shaw, J. Uszkoreit, A. Vaswani // Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), New Orleans, Louisiana, 1-6 June 2018. - Association for Computational Linguistics, 2018. - P. 464-468. DOI: 10.18653/v1/N18-2074
14. Attention is All you Need / V. Ashish, N. M. Shazeer, N. Parmar [et al.] // Advances in Neural Information Processing Systems. - 2017. - Vol. 30. - 11 p.
15. UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models / T. Xie, C. H. Wu, P. Shi [et al.] // arXiv. - 2022. - 2201.05966. DOI: 10.48550/arXiv.2201.05966
16. Yale Semantic Parsing and Text-to-SQL Challenge: сайт [Электронный ресурс]. - URL: https://yale-lily.github.io/spider (accessed 15.11.2024).
17. RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL / J. Qi, J. Tang, Z. He [et al.] // arXiv. - 2022. - 2205.06983. 10.48550/arXiv. 2205.06983. DOI: 10.48550/arXiv.2205.06983
18. Giordani, A. Corpora for Automatically Learning to Map Natural Language Questions into SQL Queries. / A. Giordani, A. Moschitti // Proceedings of the International Conference on Language Resources and Evaluation, LREC 2010, 17-23 May 2010, Valletta, Malta. - European Language Resources Association (ELRA), 2010. - P. 2336-2339.