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Explaining Natural Language query results


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.

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  1. We are extremely grateful to Fei Li and H.V. Jagadish for generously sharing with us the source code of NaLIR, and providing invaluable support.


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This research has been funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 804302), the Israeli Science Foundation (ISF) Grant No. 978/17, and the Google Ph.D. Fellowship. The contribution of Amir Gilad is part of a Ph.D. thesis research conducted at Tel Aviv University.

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Correspondence to Amir Gilad.

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Deutch, D., Frost, N. & Gilad, A. Explaining Natural Language query results. The VLDB Journal 29, 485–508 (2020).

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  • Provenance
  • CQ
  • UCQ
  • NL
  • Natural Language