X-SQL: reinforce schema representation with context
arXiv:1908.08113
Abstract
In this work, we present X-SQL, a new network architecture for the problem of parsing natural language to SQL query. X-SQL proposes to enhance the structural schema representation with the contextual output from BERT-style pre-training model, and together with type information to learn a new schema representation for down-stream tasks. We evaluated X-SQL on the WikiSQL dataset and show its new state-of-the-art performance.
References in corpus (6)
- Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
- SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
- Improving Text-to-SQL Evaluation Methodology
- Multi-Task Deep Neural Networks for Natural Language Understanding
- A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization
- IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles
Cited by in corpus (14)
- DeBERTa: Decoding-enhanced BERT with Disentangled Attention
- NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries
- GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
- TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
- Improving Text-to-SQL with Schema Dependency Learning
- Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker
- A Discrete Hard EM Approach for Weakly Supervised Question Answering
- Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
- Semantic Evaluation for Text-to-SQL with Distilled Test Suites
- SeqGenSQL -- A Robust Sequence Generation Model for Structured Query Language
- Generating Semantically Valid Adversarial Questions for TableQA
- Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering
- Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning
- "What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL