Text-To-Sql
48 papers with code • 2 benchmarks • 8 datasets
( Image credit: SyntaxSQLNet )
Benchmarks
| Trend | Dataset | Best Model | Paper Title | Paper | Code | Compare |
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SPIDER
SParC
HybridQA
CoSQL
SEDE
Greatest papers with code
IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation
PaddlePaddle/PaddleNLP
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EMNLP 2020
Our model outperforms previous state-of-the-art model by a large margin and achieves new state-of-the-art results on the two datasets.
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
PaddlePaddle/PaddleNLP
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ACL 2020
The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query.
Ranked #3 on
Semantic Parsing
on spider
SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising
In text-to-SQL task, seq-to-seq models often lead to sub-optimal performance due to limitations in their architecture.
SeqGenSQL -- A Robust Sequence Generation Model for Structured Query Language
We explore using T5 (Raffel et al. (2019)) to directly translate natural language questions into SQL statements.
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
Existing state-of-the-art approaches rely on reinforcement learning to reward the decoder when it generates any of the equivalent serializations.
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
A significant amount of the world's knowledge is stored in relational databases.
Ranked #8 on
Code Generation
on WikiSQL
Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations
Sequence-to-sequence (seq2seq) models are prevalent in semantic parsing, but have been found to struggle at out-of-distribution compositional generalization.
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
facebookresearch/tabert
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ACL 2020
Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks.
Ranked #1 on
Semantic Parsing
on WikiTableQuestions
Semantic Evaluation for Text-to-SQL with Distilled Test Suites
We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets.
Ranked #4 on
Semantic Parsing
on spider