Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing (2012.12627v2)

Abstract: We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing. BRIDGE represents the question and DB schema in a tagged sequence where a subset of the fields are augmented with cell values mentioned in the question. The hybrid sequence is encoded by BERT with minimal subsequent layers and the text-DB contextualization is realized via the fine-tuned deep attention in BERT. Combined with a pointer-generator decoder with schema-consistency driven search space pruning, BRIDGE attained state-of-the-art performance on popular cross-DB text-to-SQL benchmarks, Spider (71.1\% dev, 67.5\% test with ensemble model) and WikiSQL (92.6\% dev, 91.9\% test). Our analysis shows that BRIDGE effectively captures the desired cross-modal dependencies and has the potential to generalize to more text-DB related tasks. Our implementation is available at \url{https://github.com/salesforce/TabularSemanticParsing}.

• The paper presents the BRIDGE model that serializes natural language queries and database schemas into tagged sequences for improved Text-to-SQL translation.
• It integrates a pointer-generator decoder with schema-consistency pruning to maintain SQL syntax and enhance alignment with anchor texts.
• Extensive evaluations on Spider and WikiSQL confirm state-of-the-art accuracies, showcasing its scalability across diverse database schemas.

Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing

The paper "Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing" presents a novel approach for addressing the inherent challenges in cross-domain text-to-SQL semantic parsing. This is a critical task in NLP that involves translating natural language questions into structured SQL queries executable on various database schemas. The paper introduces the BRIDGE model, a sequential architecture leveraging BERT to align and encode textual and tabular inputs cohesively.

Key Contributions and Methodology

The authors propose BRIDGE, a lightweight yet effective framework designed to enhance cross-domain semantic parsing by jointly modeling dependencies between natural language questions and relational databases. The model is particularly noted for its capacity to handle inputs from different domains, characterized by varied database schemas and query structures. The methodology stands out due to several key innovations:

1. Tagged Sequence Representation: BRIDGE serializes both the natural language questions and the database schema into a tagged sequence. This sequence is enriched by appending cell values from the database when mentioned in the question. This hybrid sequence leverages BERT for encoding, enabling the model to effectively capture cross-modal dependencies through its deep attention architectures.
2. Pointer-Generator Decoder: The architecture is further enhanced by a pointer-generator decoder, integrated with schema-consistency-driven pruning of the search space. This approach ensures syntactical and structural adherence to SQL semantics while addressing efficiency in generation.
3. Anchor Text Use: A notable aspect of the model is the use of anchor texts—database cell values mentioned in questions—which enhance alignment with corresponding schema components. This feature improves the bridging (linking) between text mentions and schema attributes, facilitating accurate query formulation.

Results and Performance

The BRIDGE model demonstrates robust performance on established benchmarks such as Spider and WikiSQL. Achieving state-of-the-art results, the model records exact match accuracies of 71.1% (dev) and 67.5% (test) on Spider when using an ensemble approach. For WikiSQL, the model records accuracies of 92.6% (dev) and 91.9% (test). These figures underscore the potential of BRIDGE in efficiently handling a range of query complexities across diverse database schemas. Notably, the model maintains simplicity relative to other sophisticated architectures, striking a balance between comprehensibility and efficacy.

Implications and Future Directions

The work highlights several theoretical and practical implications. Theoretically, it underscores the value of leveraging pre-trained LLMs like BERT for tasks involving heterogeneous data types, such as the integration of textual and relational database information. Practically, BRIDGE offers a scalable solution that can be adapted for various domains, minimizing the need for domain-specific adjustments in semantic parsing models.

The paper also opens avenues for future research in AI and NLP:

• Model Scalability: Investigating alternate architectures that might enhance scalability while maintaining or improving the performance on larger databases and more complex queries.
• Pre-training Strategies: Exploring more comprehensive pre-training strategies that might improve the model’s ability to generalize complex relationships within and across databases.
• Explainability: Enhancing the model's interpretability to aid in understanding how query components are mapped to linguistic tokens, which could improve user trust in automation systems.

In conclusion, the BRIDGE model is a significant stride towards effective cross-domain semantic parsing, offering a promising framework adaptable to a range of applications requiring SQL query generation from natural language, while setting a robust benchmark for future explorations in this domain.