Abstract
In recent years, the Text-to-SQL task has become a research hotspot in semantic analysis. Among them, context-dependent Text-to-SQL task has received more and more attention as it meets the needs of actual scenarios. The core of the problem is how to use historical interaction information and database schema to understand the context. Most existing research ignores the structure of SQL queries and introduces low-level information such as variable names and parameters, and the mismatch problem between intents expressed in utterance and the implementation details in SQL still exists. In this paper, SemQL is applied to serve as an intermediate representation between utterance and SQL, meanwhile, the Coarse-to-Fine neural architecture is adopted to decompose decoding process of SemQL into two stages. We validated the performance of our model on SParC and CoSQL datasets, which outperforms the existing ones and achieves excellent results on both datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zelle, J.M., Mooney, R.J.: Learning to parse database queries using inductive logic programming. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, AAAI 96, IAAI 96, Portland, Oregon, USA, August 4–8, 1996, vol. 2, pp. 1050–1055. AAAI Press/The MIT Press (1996)
Zhong, V., Xiong, C., Socher, R.: Seq2SQL: generating structured queries from natural language using reinforcement learning. CoRR abs/1709.00103 (2017)
Yu, T., et al.: Spider: a large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pp. 3911–3921. Association for Computational Linguistics (2018)
Zhang, X., Yin, F., Ma, G., Ge, B., Xiao, W.: M-SQL: multi-task representation learning for single-table text2SQL generation. IEEE Access 8, 43156–43167 (2020)
Kelkar, A., Relan, R., Bhardwaj, V., Vaichal, S., Relan, P.: Bertrand-DR: improving text-to-SQL using a discriminative re-ranker. CoRR abs/2002.00557 (2020)
Cao, R., Chen, L., Chen, Z., Zhao, Y., Zhu, S., Yu, K.: LGESQL: line graph enhanced text-to-SQL model with mixed local and non-local relations. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1–6, 2021, pp. 2541–2555. Association for Computational Linguistics (2021)
LeClair, A., Haque, S., Wu, L., McMillan, C.: Improved code summarization via a graph neural network. In: ICPC 2020: 28th International Conference on Program Comprehension, Seoul, Republic of Korea, July 13–15, 2020, pp. 184–195. ACM (2020)
Huang, J., Wang, Y., Wang, Y., Dong, Y., Xiao, Y.: Relation aware semi-autoregressive semantic parsing for NL2SQL. CoRR abs/2108.00804 (2021)
Yu, T., et al.: SParC: cross-domain semantic parsing in context. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pp. 4511–4523. Association for Computational Linguistics (2019)
Yu, T., et al.: CoSQL: a conversational text-to-SQL challenge towards cross-domain natural language interfaces to databases. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3–7, 2019, pp. 1962–1979. Association for Computational Linguistics (2019)
Zhang, R., et al.: Editing-based SQL query generation for cross-domain context-dependent questions. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3–7, 2019, pp. 5337–5348. Association for Computational Linguistics (2019)
Cai, Y., Wan, X.: IGSQL: database schema interaction graph based neural model for context-dependent text-to-SQL generation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16–20, 2020, pp. 6903–6912. Association for Computational Linguistics (2020)
Yu, W., Chang, T., Guo, X., Wang, M., Wang, X.: An interaction-modeling mechanism for context-dependent text-to-SQL translation based on heterogeneous graph aggregation. Neural Netw. 142, 573–582 (2021)
Dong, L., Lapata, M.: Coarse-to-fine decoding for neural semantic parsing. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15–20, 2018, Volume 1: Long Papers, pp. 731–742. Association for Computational Linguistics (2018)
Lei, W., et al.: Re-examining the role of schema linking in text-to-SQL. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16–20, 2020, pp. 6943–6954. Association for Computational Linguistics (2020)
Guo, J., et al.: Towards complex text-to-SQL in cross-domain database with intermediate representation. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pp. 4524–4535. Association for Computational Linguistics (2019)
Hui, B., et al.: Improving text-to-SQL with schema dependency learning. CoRR abs/2103.04399 (2021)
Chen, Z., et al.: ShadowGNN: graph projection neural network for text-to-SQL parser. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6–11, 2021, pp. 5567–5577. Association for Computational Linguistics (2021)
Rubin, O., Berant, J.: SmBoP: semi-autoregressive bottom-up semantic parsing. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6–11, 2021, pp. 311–324. Association for Computational Linguistics (2021)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2–7, 2019, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics (2019)
Hwang, W., Yim, J., Park, S., Seo, M.: A comprehensive exploration on WikiSQL with table-aware word contextualization. CoRR abs/1902.01069 (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings (2015)
Acknowledgement
This work was supported by National Natural Science Foundation of China (No.61962039) and Inner Mongolia Natural Science Foundation (No.2019MS06032).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gao, X., Zhao, J. (2024). Context-Dependent Text-to-SQL Generation with Intermediate Representation. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14325. Springer, Singapore. https://doi.org/10.1007/978-981-99-7019-3_21
Download citation
DOI: https://doi.org/10.1007/978-981-99-7019-3_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7018-6
Online ISBN: 978-981-99-7019-3
eBook Packages: Computer ScienceComputer Science (R0)