Abstract
Recently, the Text-to-SQL task has received much attention. Many sophisticated neural models have been invented that achieve significant results. Most current work assumes that all the inputs are legal and the model should generate an SQL query for any input. However, in the real scenario, users are allowed to enter the arbitrary text that may not be answered by an SQL query. In this article, we focus on the issue–answerability classification for the Text-to-SQL system, which aims to distinguish the answerability of the question according to the given database schema. Existing methods concatenate the question and the database schema into a sentence, then fine-tune the pre-trained language model on the answerability classification task. In this way, the database schema is regarded as sequence text that may ignore the intrinsic structure relationship of the schema data, and the attention that represents the correlation between the question token and the database schema items is not well designed. To this end, we propose a relational Question-Schema graph framework that can effectively model the attention and relation between question and schema. In addition, a conditional layer normalization mechanism is employed to modulate the pre-trained language model to generate better question representation. Experiments demonstrate that the proposed framework outperforms all existing models by large margins, achieving new state of the art on the benchmark TRIAGESQL. Specifically, the model attains 88.41%, 78.24%, and 75.98% in Precision, Recall, and F1, respectively. Additionally, it outperforms the baseline by approximately 4.05% in Precision, 6.96% in Recall, and 6.01% in F1.
- [1] . 2016. Layer normalization. CoRR abs/1607.06450 (2016). http://arxiv.org/abs/1607.06450Google Scholar
- [2] . 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations: Conference Track (ICLR’15).Google Scholar
- [3] . 2017. Convolutional recurrent neural networks for music classification. In Proceedings of the 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’17). IEEE, Los Alamitos, CA, 2392–2396.Google Scholar
Digital Library
- [4] . 2017. Modulating early visual processing by language. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017. 6594–6604.Google Scholar
- [5] . 2019. 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’19) (Volume 1: Long and Short Papers). 4171–4186.Google Scholar
- [6] . 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research 12, 7 (2011), 2121–2159.Google Scholar
- [7] . 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017. 1024–1034.Google Scholar
- [8] . 2019. Small steps and giant leaps: Minimal Newton solvers for deep learning. In Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV’19). IEEE, Los Alamitos, CA, 4762–4771.Google Scholar
Cross Ref
- [9] . 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735–1780.Google Scholar
Digital Library
- [10] . 2021. Dynamic hybrid relation network for cross-domain context-dependent semantic parsing. CoRR abs/2101.01686 (2021).Google Scholar
- [11] . 2014. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1746–1751.
DOI: Google ScholarCross Ref
- [12] . 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR’15).Google Scholar
- [13] . 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations: Conference Track (ICLR’15). http://arxiv.org/abs/1412.6980Google Scholar
- [14] . 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations: Conference Track (ICLR’17). https://openreview.net/forum?id=SJU4ayYgl.Google Scholar
- [15] . 2020. ALBERT: A lite BERT for self-supervised learning of language representations. In Proceedings of the 8th International Conference on Learning Representations (ICLR’20).Google Scholar
- [16] . 2020. Incomplete utterance rewriting as semantic segmentation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP’20). 2846–2857.Google Scholar
Cross Ref
- [17] . 2020. Hybrid ranking network for Text-to-SQL. CoRR abs/2008.04759 (2020).Google Scholar
- [18] . 2013. Efficient estimation of word representations in vector space. In Proceedings of the 1st International Conference on Learning Representations: Workshop Track (ICLR’13).Google Scholar
- [19] . 2019. A pilot study for Chinese SQL semantic parsing. 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’19). 3652–3658.Google Scholar
Cross Ref
- [20] . 2016. Kalman-based stochastic gradient method with stop condition and insensitivity to conditioning. SIAM Journal on Optimization 26, 4 (2016), 2620–2648.Google Scholar
Digital Library
- [21] . 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT’18) (Volume 1: Long Papers). 2227–2237.Google Scholar
Cross Ref
- [22] . 1992. Acceleration of stochastic approximation by averaging. SIAM Journal on Control and Optimization 30, 4 (1992), 838–855.Google Scholar
Digital Library
- [23] . 1999. On the momentum term in gradient descent learning algorithms. Neural Networks 12, 1 (1999), 145–151.Google Scholar
Digital Library
- [24] . 2018. Improving Language Understanding by Generative Pre-Training. Retrieved January 6, 2023 from https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf.Google Scholar
- [25] . 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research 21 (2020), Article 140, 67 pages.Google Scholar
- [26] . 1988. Learning Representations by Back-Propagating Errors. MIT Press, Cambridge, MA, 696–699. Google Scholar
- [27] . 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 86 (2008), 2579–2605. http://jmlr.org/papers/v9/vandermaaten08a.html.Google Scholar
- [28] . 2017. Attention is all you need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017. 5998–6008.Google Scholar
- [29] . 2017. Graph attention networks. CoRR abs/1710.10903 (2017).Google Scholar
- [30] . 2020. RAT-SQL: Relation-aware schema encoding and linking for Text-to-SQL parsers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL’20). 7567–7578.Google Scholar
Cross Ref
- [31] . 2020. Relational graph attention network for aspect-based sentiment analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL’20). 3229–3238.Google Scholar
Cross Ref
- [32] . 2019. XLNet: Generalized autoregressive pretraining for language understanding. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 (NeurIPS’19). 5754–5764.Google Scholar
- [33] . 1998. Gradient-based learning applied to document recognition. In Proceedings of the IEEE 86, 11 (1998), 2278–2324.Google Scholar
- [34] . 2019. 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’19). 1962–1979.Google Scholar
Cross Ref
- [35] . 2018. 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.Google Scholar
Cross Ref
- [36] . 2019. SParC: Cross-domain semantic parsing in context. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.Google Scholar
Cross Ref
- [37] . 2021. An interaction-modeling mechanism for context-dependent Text-to-SQL translation based on heterogeneous graph aggregation. Neural Networks 142 (2021), 573–582. Google Scholar
Digital Library
- [38] . 2021. Similar questions correspond to similar SQL queries: A case-based reasoning approach for Text-to-SQL translation. In Case-Based Reasoning Research and Development, and (Eds.). Springer International Publishing, Cham, Switzerland, 294–308. Google Scholar
- [39] . 2020. Did you ask a good question? A cross-domain question intention classification benchmark for Text-to-SQL. CoRR abs/2010.12634 (2020). https://arxiv.org/abs/2010.12634Google Scholar
- [40] . 2017. Seq2SQL: Generating structured queries from natural language using reinforcement learning. CoRR abs/1709.00103 (2017). http://arxiv.org/abs/1709.00103Google Scholar
- [41] . 2016. Attention-based bidirectional long short-term memory networks for relation classification. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, (ACL’16) (Volume 2: Short Papers).Google Scholar
Cross Ref
Index Terms
Bravely Say I Don’t Know: Relational Question-Schema Graph for Text-to-SQL Answerability Classification
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