Towards Generalizable and Robust Text-to-SQL Parsing (2210.12674v1)

Abstract: Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries. In practice, text-to-SQL parsers often encounter various challenging scenarios, requiring them to be generalizable and robust. While most existing work addresses a particular generalization or robustness challenge, we aim to study it in a more comprehensive manner. In specific, we believe that text-to-SQL parsers should be (1) generalizable at three levels of generalization, namely i.i.d., zero-shot, and compositional, and (2) robust against input perturbations. To enhance these capabilities of the parser, we propose a novel TKK framework consisting of Task decomposition, Knowledge acquisition, and Knowledge composition to learn text-to-SQL parsing in stages. By dividing the learning process into multiple stages, our framework improves the parser's ability to acquire general SQL knowledge instead of capturing spurious patterns, making it more generalizable and robust. Experimental results under various generalization and robustness settings show that our framework is effective in all scenarios and achieves state-of-the-art performance on the Spider, SParC, and CoSQL datasets. Code can be found at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/tkk.

Towards Generalizable and Robust Text-to-SQL Parsing

The research paper titled "Towards Generalizable and Robust Text-to-SQL Parsing" addresses critical challenges in the field of natural language processing, specifically related to mapping natural language questions to SQL queries. This work attempts to enhance both the generalization and robustness of text-to-SQL parsers by proposing a novel framework named TKK, which includes Task decomposition, Knowledge acquisition, and Knowledge composition.

The uniqueness of this research lies in its comprehensive approach that goes beyond addressing isolated challenges, by focusing on three levels of generalization: i.i.d., zero-shot, and compositional. Furthermore, the TKK framework aims to make parsers more robust against input perturbations. This holistic strategy is crucial for developing parsers that can operate effectively in diverse and dynamic real-world environments.

Methodology

The TKK framework executes the learning process in three stages:

1. Task Decomposition: The text-to-SQL task is divided into subtasks, each focusing on translating a specific part of the SQL query. This step reduces the complexity of learning by allowing the model to handle simpler tasks before tackling the entire SQL query generation.
2. Knowledge Acquisition: This stage employs a multi-task learning approach to train the model on these subtasks. By doing so, the model learns to align questions with the database schema and SQL clauses, fostering more generalizable SQL knowledge, rather than memorizing patterns specific to the training data.
3. Knowledge Composition: In this final stage, the model is fine-tuned on the main task to learn the dependencies between different subtasks, ensuring the synthesized knowledge is well-integrated and robust.

Results

Empirical evaluations demonstrate the effectiveness of the TKK framework across various benchmarks, achieving state-of-the-art performance on the Spider, SParC, and CoSQL datasets. The framework significantly improves the model’s capability to manage complex queries and shows remarkable robustness against input perturbations, outperforming traditional single-stage models.

Implications

The implications of this research are significant, both theoretically and practically. Theoretically, it provides a structured approach to tackle the alignment challenge in text-to-SQL parsing by systematically decomposing the task and acquiring knowledge in stages. Practically, the robustness and generalization improvements suggest that models built on the TKK framework can be more reliably deployed in real-world applications where data variability and incompleteness are prevalent.

Future Prospects

The success of the TKK framework opens avenues for further research. One potential direction could involve extending the framework to cover other complex task formulations within artificial intelligence and machine learning, where decomposing tasks into manageable components could enhance performance. Moreover, integrating advanced techniques such as reinforcement learning or more sophisticated data augmentation strategies could further refine the robustness and adaptability of text-to-SQL parsers developed under this framework.

In conclusion, the paper presents a well-structured approach to improving the generalization and robustness of text-to-SQL parsing models through the innovative TKK framework, contributing valuable insights and methods to the field of automated querying systems.