Paper: Entropy-based Pruning for Phrase-based Machine Translation

ACL ID D12-1088
Title Entropy-based Pruning for Phrase-based Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012

Phrase-based machine translation models have shown to yield better translations than Word-based models, since phrase pairs en- code the contextual information that is needed for a more accurate translation. However, many phrase pairs do not encode any rele- vant context, which means that the transla- tion event encoded in that phrase pair is led by smaller translation events that are indepen- dent from each other, and can be found on smaller phrase pairs, with little or no loss in translation accuracy. In this work, we pro- pose a relative entropy model for translation models, that measures how likely a phrase pair encodes a translation event that is derivable using smaller translation events with similar probabilities. This model is then applied to phrase table pruning. Tests show that co...