Assessing forward-, reverse-, and average-entailer indices on natural language input from the Intelligent Tutoring System, iSTART

Philip M. McCarthy, Vasile Rus, Scott A. Crossley, Arthur C. Graesser, Danielle S. McNamara

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

11 Scopus citations

Abstract

This study reports on an experiment that analyzes a variety of entailment evaluations provided by a lexico-syntactic tool, the Entailer. The environment for these analyses is from a corpus of self-explanations taken from the Intelligent Tutoring System, iSTART. The purpose of this study is to examine how evaluations of hand-coded entailment, paraphrase, and elaboration compare to various evaluations provided by the Entailer. The evaluations include standard entailment (forward) as well as the new indices of Reverse- and Average-Entailment. The study finds that the Entailer's indices match or surpass human evaluators in making textual evaluations. The findings have important implications for providing accurate and appropriate feedback to users of Intelligent Tutoring Systems.

Original languageEnglish (US)
Title of host publicationProceedings of the 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21
Pages165-170
Number of pages6
StatePublished - 2008
Externally publishedYes
Event21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21 - Coconut Grove, FL, United States
Duration: May 15 2008May 17 2008

Publication series

NameProceedings of the 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21

Other

Other21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21
Country/TerritoryUnited States
CityCoconut Grove, FL
Period5/15/085/17/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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