SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers

TL;DR

This paper introduces SUN, a method that leverages intrinsic data and model uncertainties to enhance the robustness and accuracy of neural text-to-SQL parsers, achieving state-of-the-art results on multiple benchmarks.

Contribution

It proposes a novel uncertainty-based framework that explores semantic equivalences and model perturbations to improve parser robustness and generalization.

Findings

Significant performance improvements on five benchmark datasets.

Outperforms existing state-of-the-art methods.

Reduces sensitivity to spurious correlations in data.

Abstract

This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncertainty perspective, it is indisputable that a single SQL can be learned from multiple semantically-equivalent questions.Different from previous methods that are limited to one-to-one mapping, we propose a data uncertainty constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions (many-to-one) and learn the robust feature representations with reduced spurious associations. In this way, we can reduce the sensitivity of the learned representations and improve the robustness of the parser. From the model uncertainty perspective, there is often structural information (dependence) among the weights of neural networks. To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms strong competitors and achieves new state-of-the-art results. For reproducibility, we release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/sunsql.