Asking and evaluating natural language questions

Anselm Rothe, Brenden M. Lake, Todd M. Gureckis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The ability to ask questions during learning is a key aspect of human cognition. While recent research has suggested common principles underlying human and machine “active learning, ” the existing literature has focused on relatively simple types of queries. In this paper, we study how humans construct rich and sophisticated natural language queries to search for information in a large yet computationally tractable hypothesis space. In Experiment 1, participants were allowed to ask any question they liked in natural language. In Experiment 2, participants were asked to evaluate questions that they did not generate themselves. While people rarely asked the most informative questions in Experiment 1, they strongly preferred more informative questions in Experiment 2, as predicted by an ideal Bayesian analysis. Our results show that rigorous information-based accounts of human question asking are more widely applicable than previously studied, explaining preferences across a diverse set of natural language questions.

Original languageEnglish (US)
Title of host publicationProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
EditorsAnna Papafragou, Daniel Grodner, Daniel Mirman, John C. Trueswell
PublisherThe Cognitive Science Society
Pages2051-2056
Number of pages6
ISBN (Electronic)9780991196739
StatePublished - 2016
Event38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016 - Philadelphia, United States
Duration: Aug 10 2016Aug 13 2016

Publication series

NameProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016

Conference

Conference38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016
Country/TerritoryUnited States
CityPhiladelphia
Period8/10/168/13/16

Keywords

  • active learning
  • Bayesian modeling
  • information search
  • question asking

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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