STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing (2210.11888v2)

Abstract: In this paper, we propose a novel SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing, which leverages contextual information to enrich natural language (NL) utterance and table schema representations for text-to-SQL conversations. Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation. In addition, we construct a high-quality large-scale context-dependent text-to-SQL conversation corpus to pre-train STAR. Extensive experiments show that STAR achieves new state-of-the-art performance on two downstream benchmarks (SParC and CoSQL), significantly outperforming previous pre-training methods and ranking first on the leaderboard. We believe the release of the constructed corpus, codebase and pre-trained STAR checkpoints would push forward the research in this area. For reproducibility, we release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/star.

Overview of STaR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing

The paper introduces STaR, a SQL-guided pre-training framework designed to enhance context-dependent text-to-SQL parsing. The framework seeks to address and alleviate inherent discrepancies between tables and natural language distributions, which often lead to suboptimal performances in general pre-trained LLMs (PLMs).

Key Contributions

The novel framework introduces two pre-training objectives:

1. Schema State Tracking (SST): This objective maintains a thorough understanding of schema states across SQL conversations, treating SQL queries as evolving schema states, where each slot represents a column name, and its value is a SQL keyword.
2. Utterance Dependency Tracking (UDT): This tracks complex semantic dependencies via weighted contrastive learning, aligning semantically similar utterances and distinguishing dissimilar ones based on their respective SQL queries. SQL queries' structure and semantics are quantified through tailored similarity functions.

A high-quality, large-scale corpus composed of context-dependent text-to-SQL conversations was constructed for pre-training, enhancing the STaR model's utility beyond existing datasets.

Experimental Results

The authors present empirical evidence that STaR significantly surpasses previous models on SParC and CoSQL benchmarks, achieving new state-of-the-art performance. Specifically:

• SParC Benchmark: STaR improved both question match (QM) and interaction match (IM) metrics, securing superior accuracy levels in the leaderboard.
• CoSQL Benchmark: Consistent advancements were observed, with STaR outperforming established methods, highlighting the efficacy of incorporating SQL-guided objectives.

Implications and Future Directions

The results of this research offer substantial implications for the future of context-dependent text-to-SQL parsing. By introducing schema and utterance tracking mechanisms, STaR demonstrates the potential for models to overcome traditional barriers posed by sequential SQL queries and complex dialogues.

• Practical Implications: This approach could lead to more accurate and efficient database querying systems for users lacking SQL expertise, reducing the dependency on manually crafted SQL queries.
• Theoretical Implications: STaR signifies a new direction in capturing the nuances of SQL and natural language interactions, potentially informing future work on hybrid LLMs that integrate structured data processing with unstructured language understanding.

Speculations on Future Developments:

• Enhanced Pre-Training Objectives: Future research might expand on the SST and UDT objectives to cover more detailed SQL structures or integrate additional user intent modeling techniques.
• Broader Applicability: Expanding the framework's domain beyond SQL to other structured query languages could be explored, extending its application scope.
• Integration with Other Models: Investigating seamless integration with other pre-trained models may result in more generalized frameworks, offering robust solutions across diverse natural language processing tasks.

The release of the dataset and codebase allows for reproducibility and further research, potentially catalyzing novel advancements in pre-training techniques for text-to-SQL parsing tasks.