A Systematic Comparison of Phrase Table Pruning Techniques
Venue
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Association for Computational Linguistics, Jeju Island, Korea, pp. 972-983
Publication Year
2012
Authors
Richard Zens, Daisy Stanton, Peng Xu
BibTeX
Abstract
When trained on very large parallel corpora, the phrase table component of a
machine translation system grows to consume vast computational resources. In this
paper, we introduce a novel pruning criterion that places phrase table pruning on a
sound theoretical foundation. Systematic experiments on four language pairs under
various data conditions show that our principled approach is superior to existing
ad hoc pruning methods.
