SQL Parsing
20 papers with code • 8 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in SQL Parsing
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
IMDb Movie Reviews
ATIS
Yelp
Most implemented papers
Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph
First, we extract a schema linking graph from PLMs through a probing procedure in an unsupervised manner.
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers
alibabaresearch/damo-convai
•
•
COLING 2022
To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other.
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing
alibabaresearch/damo-convai
•
•
Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation.
Towards Generalizable and Robust Text-to-SQL Parsing
alibabaresearch/damo-convai
•
•
Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries.
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing
AlibabaResearch/DAMO-ConvAI
•
•
Recently, the pre-trained text-to-text transformer model, namely T5, though not specialized for text-to-SQL parsing, has achieved state-of-the-art performance on standard benchmarks targeting domain generalization.
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i. e., tables and columns) and the skeleton (i. e., SQL keywords).
Text-to-SQL Error Correction with Language Models of Code
Thus, we propose a novel representation for SQL queries and their edits that adheres more closely to the pre-training corpora of language models of code.
Semantic Decomposition of Question and SQL for Text-to-SQL Parsing
However, this strategy encounters two major obstacles: (1) existing datasets lack question decomposition; (2) due to the syntactic complexity of SQL, most complex queries cannot be disentangled into sub-queries that can be readily recomposed.
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
Our framework comprises a core decomposer agent for Text-to-SQL generation with few-shot chain-of-thought reasoning, accompanied by two auxiliary agents that utilize external tools or models to acquire smaller sub-databases and refine erroneous SQL queries.
When is Tree Search Useful for LLM Planning? It Depends on the Discriminator
osu-nlp-group/llm-planning-eval
•
•
In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method.
