Explaining Natural Language Query Results

TL;DR

This paper introduces a method for generating detailed natural language explanations for database query results by transforming provenance information, with solutions for effective NL presentation and an end-to-end system implementation.

Contribution

It presents a novel approach to converting provenance data into natural language explanations, enhancing transparency and interpretability of query results.

Findings

User study confirms high quality of explanations

Proposed methods are scalable to large provenance data

Effective NL presentation improves user understanding

Abstract

Multiple lines of research have developed Natural Language (NL) interfaces for formulating database queries. We build upon this work, but focus on presenting a highly detailed form of the answers in NL. The answers that we present are importantly based on the provenance of tuples in the query result, detailing not only the results but also their explanations. We develop a novel method for transforming provenance information to NL, by leveraging the original NL query structure. Furthermore, since provenance information is typically large and complex, we present two solutions for its effective presentation as NL text: one that is based on provenance factorization, with novel desiderata relevant to the NL case, and one that is based on summarization. We have implemented our solution in an end-to-end system supporting questions, answers and provenance, all expressed in NL. Our experiments, including a user study, indicate the quality of our solution and its scalability.