Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary
input modality to direct manipulation for visual analytics can provide an engaging user …

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 …

For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing
the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are …

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 …

One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the
structural property of the SQL queries, the seq2seq model takes the responsibility of parsing …

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

To bridge the gap between users and data, numerous text-to-SQL systems have been
developed that allow users to pose natural language questions over relational databases …

We present Spider, a large-scale, complex and cross-domain semantic parsing and text-to-
SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 …

A significant amount of the world's knowledge is stored in relational databases. However,
the ability for users to retrieve facts from a database is limited due to a lack of understanding …

We present a neural approach called IRNet for complex and cross-domain Text-to-SQL.
IRNet aims to address two challenges: 1) the mismatch between intents expressed in natural …