Active learning-based elicitation for semi-supervised word alignment

Vamshi Ambati, Stephan Vogel, Jaime Carbonell

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

3 Citations (Scopus)

Abstract

Semi-supervised word alignment aims to improve the accuracy of automatic word alignment by incorporating full or partial manual alignments. Motivated by standard active learning query sampling frameworks like uncertainty-, margin- and query-by-committee sampling we propose multiple query strategies for the alignment link selection task. Our experiments show that by active selection of uncertain and informative links, we reduce the overall manual effort involved in elicitation of alignment link data for training a semi-supervised word aligner.

Original languageEnglish
Title of host publicationACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Pages365-370
Number of pages6
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 - Uppsala, Sweden
Duration: 11 Jul 201016 Jul 2010

Other

Other48th Annual Meeting of the Association for Computational Linguistics, ACL 2010
CountrySweden
CityUppsala
Period11/7/1016/7/10

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ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Ambati, V., Vogel, S., & Carbonell, J. (2010). Active learning-based elicitation for semi-supervised word alignment. In ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 365-370)