Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing (1905.06241v2)

Abstract: Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In Spider, a recently-released text-to-SQL dataset, new and complex DBs are given at test time, and so the structure of the DB schema can inform the predicted SQL query. In this paper, we present an encoder-decoder semantic parser, where the structure of the DB schema is encoded with a graph neural network, and this representation is later used at both encoding and decoding time. Evaluation shows that encoding the schema structure improves our parser accuracy from 33.8% to 39.4%, dramatically above the current state of the art, which is at 19.7%.

• The paper introduces an encoder-decoder parser that integrates graph neural networks to represent relational database schemas effectively.
• It converts database schemas into graphs with nodes for tables and columns and edges for relational ties, enhancing question-conditioned representations.
• Experimental results on Spider demonstrate accuracy improvements from 19.7% to 39.4%, highlighting significant gains for complex multi-table queries.

Graph Neural Networks for Text-to-SQL Parsing: Enhancing Schema Representations

This paper presents advancements in the domain of semantic parsing, particularly emphasizing the conversion of natural language questions into SQL queries. The predominant challenge addressed by the authors is the neglect of relational database schema structures historically seen in parsing methodologies. With the introduction of Spider, a robust text-to-SQL dataset, this paper proposes using Graph Neural Networks (GNNs) to encapsulate the DB schema structure effectively. The approach leverages schema structures at both encoding and decoding phases of semantic parsing, yielding notable improvements in parser accuracy.

Core Contributions

The essence of the paper lies in the formulation of an encoder-decoder semantic parser that incorporates GNNs to model database schema. The parser treats the schema as a graph, allowing a detailed representation of its structure. Several innovations underlie the parser's integration of schema structure:

1. Schema-to-Graph Conversion: The schema is represented as a graph composed of nodes and edges, where nodes are tables and columns, and edges represent various relational ties such as foreign and primary keys.
2. Graph Neural Network Representation: Each node in the schema graph is imbued with an initial embedding that considers the relevance of schema items conditioned on the query. The relevance score is derived from the maximum linking probability of a schema item to any word in the query.
3. Question-Conditioned Relevance: The model applies a relevance-driven soft pruning of the schema graph, focusing computational resources only on potentially pertinent schema portions for given queries.
4. Enhanced Encoder-Decoder: The encoder enriches question word representation by incorporating schema-aware embeddings, while the decoder employs a self-attention mechanism to refine predictions, emphasizing schema items related to the previously decoded elements.

Experimental Insights

Critical evaluation on the Spider dataset demonstrated the parser's superior performance relative to prior state-of-the-art models, significantly improving parsing accuracy from 19.7% to 39.4%. Notable is the performance uplift in complex queries involving multiple tables, where accuracy ascended remarkably from 14.6% to 26.8% in multi-table scenarios due to schema structural enhancements.

Ablation studies assessed the individual contributions of the GNN components, including self-attention and relevance mechanisms. These analyses confirmed the integral role of each component, with the absence of self-attention reducing performance, particularly in multi-table contexts, emphasizing its relevance in schema-informed parsing.

Implications and Future Work

The proposed framework underscores the significance of encoding relational DB schema structures in semantic parsing tasks. The model provides a pathway to improve zero-shot parsing capabilities where DB schemas are unseen during training. Practically, this advancement portends enhanced query generation in enterprise environments with complex, dynamic databases.

The successful application of GNNs in schema representation invites further exploration. Future research may delve into refining question-conditioned relevance scores or enhancing graph pruning techniques to further amplify parsing precision. Moreover, the scalability of this approach across different DB types and query languages could be another avenue for exploration.

Conclusion

By framing the DB schema as a graph and employing GNNs for representation in text-to-SQL parsing, this paper substantially elevates the capabilities of semantic parsers in handling complex, unseen schemas. The model's accuracy improvements and detailed experimental validation position it as a strong candidate for practical applications in database management systems, where SQL parsing accuracy is paramount.