SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers (2209.06442v2)

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.

Exploring Intrinsic Uncertainties in Text-to-SQL Parsers

The paper, "Sun: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers," presents an innovative approach to improving text-to-SQL parsing by addressing inherent uncertainties intrinsic to natural language processing tasks. The authors propose a method called Sun, which focuses on enhancing performance through the analysis of data and model uncertainties in neural network-based parsers.

Summary and Methodology

Text-to-SQL parsing involves converting natural language (NL) questions into structured query language (SQL) queries, facilitating broader access to relational databases. Existing models predominantly focus on one-to-one mappings between NL questions and SQL queries, neglecting the many-to-one nature where multiple semantically-equivalent questions may correspond to a single SQL query. Additionally, they often overlook model uncertainty related to the structural dependencies within neural networks.

The Sun method introduces two primary components:

1. Data Uncertainty Constraint: This component leverages the inherent variability where multiple NL questions map to a single SQL query. By identifying and learning from the complementary semantic information in these variations, the goal is to develop feature representations with reduced sensitivity and fewer spurious associations.
2. Model Uncertainty Constraint: This aims at addressing the dependencies and uncertainties in model weights. By ensuring consistency in output representations from perturbed encoding networks, the stability and generalizability of the model are enhanced.

Experimental Evaluation

The paper reports extensive experiments on five benchmark datasets, including Spider, Syn, Dk, Realistic, and Squall, demonstrating that the Sun method achieves superior results across various challenging setups. Notably, the method significantly improved exact match and execution accuracy scores, indicating its efficacy in both understanding and generating accurate SQL queries.

Results

The Sun method achieved state-of-the-art results in Spiders and other complex settings. For instance, on the Spider benchmark, it improved exact match scores by 1.7% over strong baseline models. This improvement was consistent across other datasets as well, such as achieving a 4.3% increase in accuracy on the Dk dataset, underscoring the robustness and adaptability of the method.

Implications and Future Developments

The exploration of intrinsic uncertainties in text-to-SQL parsers not only enhances semantic parsing robustness but also sets a precedent for future research in natural language interfaces and AI systems. The method’s model-agnostic nature suggests broad applicability across various existing parsing architectures.

Future developments could explore extending these uncertainty modeling techniques to other domains where semantically-equivalent expressions or structural dependencies pose challenges. Additionally, integrating Sun with more advanced LLMs and exploring its adaptability to real-time applications could yield substantial advancements in natural language processing interfaces.

The paper robustly demonstrates the utility of incorporating both data and model uncertainty modeling into text-to-SQL parsing, leading to enhanced accuracy, stability, and generalization. As such, it opens avenues for further research into building more resilient and adaptable AI systems.