Skip to main content
Active Elicitation of Data for Word Alignment
Institute for Software Research
  • Vamshi Ambati, Carnegie Mellon University
  • Stephan Vogel, Carnegie Mellon University
  • Jaime G. Carbonell, Carnegie Mellon University
Date of Original Version
Working Paper
Rights Management
All Rights Reserved
Abstract or Description
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 semisupervised word aligner.
Citation Information
Vamshi Ambati, Stephan Vogel and Jaime G. Carbonell. "Active Elicitation of Data for Word Alignment" (2010)
Available at: