Answering Count Queries with Explanatory Evidence

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

  • Shrestha Ghosh - , Saarland University, Max Planck Institute for Informatics (Author)
  • Simon Razniewski - , Max Planck Institute for Informatics (Author)
  • Gerhard Weikum - , Max Planck Institute for Informatics (Author)

Abstract

A challenging case in web search and question answering are count queries, such as"number of songs by John Lennon''. Prior methods merely answer these with a single, and sometimes puzzling number or return a ranked list of text snippets with different numbers. This paper proposes a methodology for answering count queries with inference, contextualization and explanatory evidence. Unlike previous systems, our method infers final answers from multiple observations, supports semantic qualifiers for the counts, and provides evidence by enumerating representative instances. Experiments with a wide variety of queries show the benefits of our method. To promote further research on this underexplored topic, we release an annotated dataset of 5k queries with 200k relevant text spans.

Details

Original languageEnglish
Title of host publicationSIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages2415-2419
Number of pages5
ISBN (electronic)9781450387323
Publication statusPublished - 7 Jul 2022
Peer-reviewedYes
Externally publishedYes

Conference

Title45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Abbreviated titleACM SIGIR 2022
Conference number45
Duration11 - 15 July 2022
Website
LocationCírculo de Bellas Artes & Online
CityMadrid
CountrySpain

External IDs

ORCID /0000-0002-5410-218X/work/185318189

Keywords

Keywords

  • count queries, explainable ai, question answering