Paper: A Systematic Comparison of Phrase Table Pruning Techniques

ACL ID D12-1089
Title A Systematic Comparison of Phrase Table Pruning Techniques
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

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 in- troduce 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.