End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task
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Abstract (arXiv)
In this work, we focus on two crucial components in the cross-domain text-to-SQL semantic parsing task: schema linking and value filling. To encourage the model to learn better encoding ability, we propose a column selection auxiliary task to empower the encoder with the relevance matching capability by using explicit learning targets. Furthermore, we propose two value filling methods to build the bridge from the existing zero-shot semantic parsers to real-world applications, considering most of the existing parsers ignore the values filling in the synthesized SQL. With experiments on Spider, our proposed framework improves over the baselines on the execution accuracy and exact set match accuracy when database contents are unavailable, and detailed analysis sheds light on future work.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 21 references that are in the map. First posted on arXiv 17 Jun 2021.
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18 of 21 references.
References · 18
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
- 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
- 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
- 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
- 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
- 2020Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsingcited by 133
- 2018Coarse-to-Fine Decoding for Neural Semantic Parsingcited by 110
- 2017Learning a Neural Semantic Parser from User Feedbackcited by 98
- 2018TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generationcited by 81
- 2019Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questionscited by 80
6 more
- 2020Grounded Adaptation for Zero-shot Executable Semantic Parsingcited by 76
- 2018SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Taskcited by 74
- 2019Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsingcited by 74
- 2020Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Trainingcited by 65
- 2020RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databasescited by 63
- 2019Global Reasoning over Database Structures for Text-to-SQL Parsingcited by 43
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