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 …

Question answering has emerged as an intuitive way of querying structured data sources
and has attracted significant advancements over the years. A large body of recent work on …

We present SQLova, the first Natural-language-to-SQL (NL2SQL) model to achieve human
performance in WikiSQL dataset. We revisit and discuss diverse popular methods in …

While mainstream machine learning methods are known to have limited ability to
compositionally generalize, new architectures and techniques continue to be proposed to …

We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models.
Our method distills a small test suite of databases that achieves high code coverage for the …

Relational databases are storage for a massive amount of data. Knowledge of structured
query language is a prior requirement to access that data. That is not possible for all non …

We present GraPPa, an effective pre-training approach for table semantic parsing that learns
a compositional inductive bias in the joint representations of textual and tabular data. We …

Text-to-SQL is the problem of converting a user question into an SQL query, when the
question and database are given. In this article, we present a neural network approach …

Converting natural language text into executable SQL queries significantly impacts the
healthcare domain, specifically when applied to electronic medical records. Given that …