Abstract
In text-to-SQL tasks — as in much of NLP — compositional generalization is a major challenge: neural networks struggle with compositional generalization where training and test distributions differ. However, most recent attempts to improve this are based on word-level synthetic data or specific dataset splits to generate compositional biases. In this work, we propose a clause-level compositional example generation method. We first split the sentences in the Spider text-to-SQL dataset into sub-sentences, annotating each sub-sentence with its corresponding SQL clause, resulting in a new dataset Spider-SS. We then construct a further dataset, Spider-CG, by composing Spider-SS sub-sentences in different combinations, to test the ability of models to generalize compositionally. Experiments show that existing models suffer significant performance degradation when evaluated on Spider-CG, even though every sub-sentence is seen during training. To deal with this problem, we modify a number of state-of-the-art models to train on the segmented data of Spider-SS, and we show that this method improves the generalization performance.- Anthology ID:
- 2022.findings-naacl.62
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 831–843
- Language:
- URL:
- https://aclanthology.org/2022.findings-naacl.62
- DOI:
- 10.18653/v1/2022.findings-naacl.62
- Cite (ACL):
- Yujian Gan, Xinyun Chen, Qiuping Huang, and Matthew Purver. 2022. Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 831–843, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment (Gan et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.findings-naacl.62.pdf
- Code
- ygan/spiderss-spidercg