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 …

Translating users' natural language queries (NL) into SQL queries (ie, NL2SQL) can
significantly reduce barriers to accessing relational databases and support various …

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 …

Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-
based natural language (NL) understanding tasks. Such models are typically trained on free …

When translating natural language questions into SQL queries to answer questions from a
database, contemporary semantic parsing models struggle to generalize to unseen …

We present BRIDGE, a powerful sequential architecture for modeling dependencies
between natural language questions and relational databases in cross-DB semantic …

Existing studies on question answering on knowledge bases (KBQA) mainly operate with
the standard iid assumption, ie, training distribution over questions is the same as the test …

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 …

The task of converting a natural language question into an executable SQL query, known as
text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based …