Papers SQL Parsing
“SQL Parsing” 태그가 달린 논문 50편 · 필터 해제
GP: Context-free Grammar Pre-training for Text-to-SQL Parsers
A new method for Text-to-SQL parsing, Grammar Pre-training (GP), is proposed to decode deep relations between question and database. Firstly, to better utilize the information of databases, a random value is added behind…
DecoderSentenceSQL ParsingText to SQL+1Optimizing Deeper Transformers on Small Datasets
It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layers on top of pre-trained models during fi…
Reading ComprehensionSemantic ParsingSQL ParsingText to SQL+1DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset
Due to the lack of labeled data, previous research on text-to-SQL parsing mainly focuses on English. Representative English datasets include ATIS, WikiSQL, Spider, etc. This paper presents DuSQL, a larges-scale and pragm…
SQL ParsingText to SQLText-To-SQLService-oriented Text-to-SQL Parsing
The information retrieval from relational database requires professionals who has an understanding of structural query language such as SQL. TEXT2SQL models apply natural language inference to enable user interacting the…
Information RetrievalNatural Language InferenceRetrievalSQL Parsing+3A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing
In Text-to-SQL semantic parsing, selecting the correct entities (tables and columns) for the generated SQL query is both crucial and challenging; the parser is required to connect the natural language (NL) question and t…
Graph Neural NetworkSemantic ParsingSQL ParsingText to SQL+1Graph Enhanced Cross-Domain Text-to-SQL Generation
Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations. Existing…
Natural Language UnderstandingSemantic ParsingSQL ParsingText to SQL+2Global Reasoning over Database Structures for Text-to-SQL Parsing
State-of-the-art semantic parsers rely on auto-regressive decoding, emitting one symbol at a time. When tested against complex databases that are unobserved at training time (zero-shot), the parser often struggles to sel…
Graph Neural NetworkSemantic ParsingSQL ParsingText to SQL+1Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In Spider, a recently-r…
DecoderGraph Neural NetworkSQL ParsingText to SQL+1Improving Text-to-SQL Evaluation Methodology
To be informative, an evaluation must measure how well systems generalize to realistic unseen data. We identify limitations of and propose improvements to current evaluations of text-to-SQL systems. First, we compare hum…
SQL ParsingText to SQLText-To-SQLLearning a Neural Semantic Parser from User Feedback
We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention. To achieve this…
SQL Parsing