Answering Count Queries with Explanatory Evidence

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

  • Shrestha Ghosh - , Universität des Saarlandes, Max-Planck-Institut für Informatik (Autor:in)
  • Simon Razniewski - , Max-Planck-Institut für Informatik (Autor:in)
  • Gerhard Weikum - , Max-Planck-Institut für Informatik (Autor:in)

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

OriginalspracheEnglisch
TitelSIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Herausgeber (Verlag)Association for Computing Machinery, Inc
Seiten2415-2419
Seitenumfang5
ISBN (elektronisch)9781450387323
PublikationsstatusVeröffentlicht - 7 Juli 2022
Peer-Review-StatusJa
Extern publiziertJa

Konferenz

Titel45th International ACM SIGIR Conference on Research and Development in Information Retrieval
KurztitelACM SIGIR 2022
Veranstaltungsnummer45
Dauer11 - 15 Juli 2022
Webseite
OrtCírculo de Bellas Artes & Online
StadtMadrid
LandSpanien

Externe IDs

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

Schlagworte

Schlagwörter

  • count queries, explainable ai, question answering