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

• URL: http://arxiv.org/abs/2012.12627v2
• Date: Thu, 31 Dec 2020 01:02:40 GMT
• Title: Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
• Authors: Xi Victoria Lin and Richard Socher and Caiming Xiong
• Abstract summary: 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. BRIDGE attained state-of-the-art performance on popular cross-DB text-to- relational benchmarks. Our analysis shows that BRIDGE effectively captures the desired cross-modal dependencies and has the potential to generalize to more text-DB related tasks.
• Score: 110.97778888305506
• License: http://creativecommons.org/licenses/by/4.0/
• 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}.

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