Efficient search for transformation-based inference

Asher Stern, Roni Stern, Ido Dagan, Ariel Felner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

This paper addresses the search problem in textual inference, where systems need to infer one piece of text from another. A prominent approach to this task is attempts to transform one text into the other through a sequence of inference-preserving transformations, a.k.a. a proof, while estimating the proof's validity. This raises a search challenge of finding the best possible proof. We explore this challenge through a comprehensive investigation of prominent search algorithms and propose two novel algorithmic components specifically designed for textual inference: a gradient-style evaluation function, and a locallookahead node expansion method. Evaluations, using the open-source system, BIUTEE, show the contribution of these ideas to search efficiency and proof quality.

Original languageEnglish
Title of host publication50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference
Pages283-291
Number of pages9
StatePublished - 1 Dec 2012
Event50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Jeju Island, Korea, Republic of
Duration: 8 Jul 201214 Jul 2012

Publication series

Name50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference
Volume1

Conference

Conference50th Annual Meeting of the Association for Computational Linguistics, ACL 2012
Country/TerritoryKorea, Republic of
CityJeju Island
Period8/07/1214/07/12

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software

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