xDBTagger: Explainable Natural Language Interface to Databases Using Keyword Mappings and Schema Graph

TL;DR

xDBTagger is an explainable natural language interface to databases that provides transparent SQL translation through a hybrid pipeline, improving interpretability and efficiency over existing systems.

Contribution

It introduces a hybrid, explainable translation pipeline for NLIDB that offers both textual and visual explanations, enhancing transparency and user understanding.

Findings

Achieves high accuracy in SQL translation

Provides effective textual and visual explanations

Translates queries up to 10,000 times more efficiently

Abstract

Translating natural language queries (NLQ) into structured query language (SQL) in interfaces to relational databases is a challenging task that has been widely studied by researchers from both the database and natural language processing communities. Numerous works have been proposed to attack the natural language interfaces to databases (NLIDB) problem either as a conventional pipeline-based or an end-to-end deep-learning-based solution. Nevertheless, regardless of the approach preferred, such solutions exhibit black-box nature, which makes it difficult for potential users targeted by these systems to comprehend the decisions made to produce the translated SQL. To this end, we propose xDBTagger, an explainable hybrid translation pipeline that explains the decisions made along the way to the user both textually and visually. We also evaluate xDBTagger quantitatively in three real-world relational databases. The evaluation results indicate that in addition to being fully interpretable, xDBTagger is effective in terms of accuracy and translates the queries more efficiently compared to other state-of-the-art pipeline-based systems up to 10000 times.