Explaining Queries over Web Tables to Non-Experts

TL;DR

This paper introduces methods to explain formal queries generated by natural language interfaces over web tables, making them understandable to non-expert users and improving interface reliability.

Contribution

It presents two novel explanation techniques—NL translation and graphic provenance visualization—for formal queries in web table NL interfaces, enhancing user understanding and trust.

Findings

User study shows improved query correctness understanding.

Enhanced interface reliability through explanations.

Methods increase user trust and comprehension.

Abstract

Designing a reliable natural language (NL) interface for querying tables has been a longtime goal of researchers in both the data management and natural language processing (NLP) communities. Such an interface receives as input an NL question, translates it into a formal query, executes the query and returns the results. Errors in the translation process are not uncommon, and users typically struggle to understand whether their query has been mapped correctly. We address this problem by explaining the obtained formal queries to non-expert users. Two methods for query explanations are presented: the first translates queries into NL, while the second method provides a graphic representation of the query cell-based provenance (in its execution on a given table). Our solution augments a state-of-the-art NL interface over web tables, enhancing it in both its training and deployment phase. Experiments, including a user study conducted on Amazon Mechanical Turk, show our solution to improve both the correctness and reliability of an NL interface.