Paper: Active Learning-Based Elicitation for Semi-Supervised Word Alignment

ACL ID P10-2067
Title Active Learning-Based Elicitation for Semi-Supervised Word Alignment
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

Semi-supervised word alignment aims to improve the accuracy of automatic word alignment by incorporating full or par- tial 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.