1 research outputs found
An End-to-end Neural Natural Language Interface for Databases
The ability to extract insights from new data sets is critical for decision
making. Visual interactive tools play an important role in data exploration
since they provide non-technical users with an effective way to visually
compose queries and comprehend the results. Natural language has recently
gained traction as an alternative query interface to databases with the
potential to enable non-expert users to formulate complex questions and
information needs efficiently and effectively. However, understanding natural
language questions and translating them accurately to SQL is a challenging
task, and thus Natural Language Interfaces for Databases (NLIDBs) have not yet
made their way into practical tools and commercial products.
In this paper, we present DBPal, a novel data exploration tool with a natural
language interface. DBPal leverages recent advances in deep models to make
query understanding more robust in the following ways: First, DBPal uses a deep
model to translate natural language statements to SQL, making the translation
process more robust to paraphrasing and other linguistic variations. Second, to
support the users in phrasing questions without knowing the database schema and
the query features, DBPal provides a learned auto-completion model that
suggests partial query extensions to users during query formulation and thus
helps to write complex queries