Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
arXiv:1909.00786
Abstract
We focus on the cross-domain context-dependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and their corresponding SQL queries tend to overlap, we utilize the interaction history by editing the previous predicted query to improve the generation quality. Our editing mechanism views SQL as sequences and reuses generation results at the token level in a simple manner. It is flexible to change individual tokens and robust to error propagation. Furthermore, to deal with complex table structures in different domains, we employ an utterance-table encoder and a table-aware decoder to incorporate the context of the user utterance and the table schema. We evaluate our approach on the SParC dataset and demonstrate the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch. Our code is available at https://github.com/ryanzhumich/sparc_atis_pytorch.
EMNLP 2019
References in corpus (12)
- Sequence to Sequence Learning with Neural Networks
- Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
- 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
- A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization
- A Retrieve-and-Edit Framework for Predicting Structured Outputs
- Learning to Represent Edits
- FlowQA: Grasping Flow in History for Conversational Machine Comprehension
- IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles
- CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
- Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
Cited by in corpus (21)
- CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
- TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
- Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker
- Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
- GP: Context-free Grammar Pre-training for Text-to-SQL Parsers
- Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
- Photon: A Robust Cross-Domain Text-to-SQL System
- LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations
- Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL
- An Imitation Game for Learning Semantic Parsers from User Interaction
- An Investigation Between Schema Linking and Text-to-SQL Performance
- IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation
- End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task
- ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser
- Few-Shot Semantic Parsing for New Predicates
- Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
- SPARQLing Database Queries from Intermediate Question Decompositions
- Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering
- Reducing the impact of out of vocabulary words in the translation of natural language questions into SPARQL queries
- KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
- Context Dependent Semantic Parsing: A Survey