A Systematic Comparison of Phrase Table Pruning Techniques
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.
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.