Scaling up online question answering via similar question retrieval

Chase Geigle, Chengxiang Zhai

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

Abstract

Faced with growing class sizes and the dawn of the MOOC, educators are in need of tools to help them cope with the growing number of questions asked in large classes since manually answering all the questions in a timely manner is infeasible. In this paper, we propose to exploit historical question/answer data accumulated for the same or similar classes as a basis for automatically answering previously asked questions via the use of information retrieval techniques. We further propose to leverage resolved questions to create test collections for quantitative evaluation of a question retrieval algorithm without requiring additional human effort. Using this evaluation methodology, we study the effectiveness of state of the art retrieval techniques for this special retrieval task, and perform error analysis to inform future directions.

Original languageEnglish (US)
Title of host publicationL@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale
PublisherAssociation for Computing Machinery, Inc
Pages257-260
Number of pages4
ISBN (Electronic)9781450337267
DOIs
StatePublished - Apr 25 2016
Event3rd Annual ACM Conference on Learning at Scale, L@S 2016 - Edinburgh, United Kingdom
Duration: Apr 25 2016Apr 26 2016

Publication series

NameL@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale

Other

Other3rd Annual ACM Conference on Learning at Scale, L@S 2016
CountryUnited Kingdom
CityEdinburgh
Period4/25/164/26/16

Keywords

  • Community question answering
  • Information retrieval

ASJC Scopus subject areas

  • Education
  • Software
  • Computer Science Applications
  • Computer Networks and Communications

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