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

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arxiv 2012.12627 v2 pith:6BK6N3UZ submitted 2020-12-23 cs.CL cs.AIcs.DBcs.LG

Xi Victoria Lin , Richard Socher , Caiming Xiong This is my paper · ORCID
classification cs.CLcs.AIcs.DBcs.LG
keywords bridgebertcross-dbdependenciesparsingquestionsemanticsequence
verification ladder T0 review T1 audit T2 compute T3 formal
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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}.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. STARE at the Structure: Steering ICL Exemplar Selection with Structural Alignment

    cs.CL 2025-08 conditional novelty 6.0 of 10

    A structure-aware exemplar retriever with a hidden-state syntactic injection module improves in-context semantic parsing across four benchmarks.

  2. DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph

    cs.AI 2025-05 conditional novelty 6.0 of 10

    DCG-SQL retrieves text-to-SQL demonstrations by embedding a question-to-schema link graph, improving execution accuracy on Spider by up to about 10 points over random demonstrations on small LLMs.

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