Text-to-SQL parsing is an essential and challenging task. The goal of text-to-SQL parsing is
to convert a natural language (NL) question to its corresponding structured query language …

Text-to-SQL parsing, which aims at converting natural language instructions into executable
SQLs, has gained increasing attention in recent years. In particular, GPT-4 and Claude-2 …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

The task of text-to-SQL parsing, which aims at converting natural language questions into
executable SQL queries, has garnered increasing attention in recent years. One of the major …

Recent progress in language model pre-training has achieved a great success via
leveraging large-scale unstructured textual data. However, it is still a challenge to apply pre …

This work aims to tackle the challenging heterogeneous graph encoding problem in the text-
to-SQL task. Previous methods are typically node-centric and merely utilize different weight …

Relational structures such as schema linking and schema encoding have been validated as
a key component to qualitatively translating natural language into SQL queries. However …

Most recently, there has been significant interest in learning contextual representations for
various NLP tasks, by leveraging large scale text corpora to train powerful language models …

Addressing the mismatch between natural language descriptions and the corresponding
SQL queries is a key challenge for text-to-SQL translation. To bridge this gap, we propose …

Generic unstructured neural networks have been shown to struggle on out-of-distribution
compositional generalization. Compositional data augmentation via example recombination …