SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (D18-1)
Copied to clipboard
| Challenge: | Existing studies in text-to-SQL do not require generating complex SQL queries with multiple clauses or sub-queries. |
| Approach: | They propose a syntax tree network to address the complex text-to-SQL generation task. |
| Outcome: | The proposed model outperforms the current state-of-the-art model by 9.5% on a large text-to-SQL corpus. |
Similar Papers
Graph Enhanced Cross-Domain Text-to-SQL Generation (D19-53)
Copied to clipboard
| Challenge: | Existing deep learning approaches for semantic parsing do not generalize to unseen data sets . existing benchmarks have shown text-to-SQL parsers do not generally perform well to unsen SQL queries. |
| Approach: | They propose a new cross-domain learning scheme to perform text-to-SQL translation . they demonstrate its use on a large-scale cross- domain text- to-Sql data set Spider . |
| Outcome: | The proposed learning scheme improves on a large-scale text-to-SQL data set. |
Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation (D19-1)
Copied to clipboard
| Challenge: | Existing deep learning approaches for text-to-SQL generation are limited to the WikiSQl dataset . a novel clause-wise decoding neural network model can be used to generate complex queries over multiple databases . |
| Approach: | They propose a SQL clause-wise decoding neural architecture with a schema encoder to address the Spider task. |
| Outcome: | The proposed model achieves 4.6% accuracy gain on the Spider dataset and 9.8% accuracy gain in test and dev sets. |
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation (P19-1)
Copied to clipboard
| Challenge: | IRNet synthesizes SQL queries in an end-to-end manner, but it yields unsatisfactory performance on public benchmarks. |
| Approach: | They propose a neural approach called IRNet for complex and cross-domain Text-to-SQL. |
| Outcome: | IRNet achieves 46.7% accuracy on the Spider benchmark, a 19.5% improvement over state-of-the-art approaches. |
DuoRAT: Towards Simpler Text-to-SQL Models (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent text-to-SQL models can translate natural language questions to corresponding SQL queries on unseen databases. |
| Approach: | They propose a re-implementation of the RAT-SQL model that uses only relation-aware or vanilla transformers as the building blocks. |
| Outcome: | The proposed model is based on the spider dataset and shows it can be used on large databases without human intervention. |
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (D18-1)
Copied to clipboard
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, Dragomir Radev
| Challenge: | Existing datasets for semantic parsing are too small in terms of number of programs for training modern data-intensive models. |
| Approach: | They propose a large-scale complex and cross-domain semantic parsing task for a database . they use a dataset with 10,181 questions and 5,693 unique complex SQL queries . |
| Outcome: | The proposed task is different from previous tasks because it uses the same database and program . the best model achieves only 9.7% exact matching accuracy on a database split setting. |
Knowledge Base Construction for Knowledge-Augmented Text-to-SQL (2025.findings-acl)
Copied to clipboard
Jinheon Baek, Horst Samulowitz, Oktie Hassanzadeh, Dharmashankar Subramanian, Sola Shirai, Alfio Gliozzo, Debarun Bhattacharjya
| Challenge: | Existing approaches to translate natural language queries into SQL statements are limited in their parametric knowledge of the database schemas. |
| Approach: | They propose to construct a knowledge base for text-to-SQL, a foundational source of knowledge, from which we retrieve and generate the necessary knowledge for given queries. |
| Outcome: | The proposed approach outperforms baselines on multiple text-to-SQL datasets and shows that it is practical and reliable. |
A Review of Cross-Domain Text-to-SQL Models (2020.aacl-srw)
Copied to clipboard
| Challenge: | WikiSQL and Spider are cross-domain text-to-SQl datasets that have attracted much attention from the research community. |
| Approach: | They propose to divide top models into two paradigms and evaluate their models for schema linking, pretrained word embeddings, reasoning assistance modules. |
| Outcome: | The proposed models have over 90% execution accuracy, the authors show . the proposed models are more complex and more complex than the proposed ones . |
Semantic Parsing with Syntax- and Table-Aware SQL Generation (P18-1)
Copied to clipboard
Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou
| Challenge: | Existing approaches generate a SQL query word-by-word but results are incorrect or not executable due to mismatch between question words and table contents. |
| Approach: | They propose a generative model to map natural language questions into SQL queries. |
| Outcome: | The proposed model significantly improves state-of-the-art execution accuracy from 69.0% to 74.4% on a large question- SQL dataset. |
SQL-to-Text Generation with Graph-to-Sequence Model (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to generate SQL-to-text using seq2seq models do not capture graph-structured information in SQL query. |
| Approach: | They propose a graph-to-sequence model to encode global structure information into node embeddings. |
| Outcome: | The proposed model outperforms the Seq2Seq and Tree2Sq baselines on the WikiSQL and Stackoverflow datasets. |
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion (2022.acl-long)
Copied to clipboard
| Challenge: | Existing text-to-SQL parsers struggle with out-of-domain generalization problems, arguing that they lack the ability to match domain specific phrases to composite operations over columns. |
| Approach: | They propose to use a synthetic dataset and a re-purposed train/test split to quantify out-of-domain generalization over column operations to address this problem. |
| Outcome: | The proposed method outperforms baseline parsers on the domain generalization problem, while boosting the underlying parser’ overall performance by 13.8% relative accuracy gain (5.1% absolute). |