Training Phrase Translation Models with Leaving-One-Out

Several attempts have been made to learn phrase translation probabilities for phrasebased statistical machine translation that go beyond pure counting of phrases in word-aligned training data. Most approaches report problems with overfitting. We describe a novel leavingone-out approach to prevent over-fitting that allows us to train phrase models that show improved translation performance on the WMT08 Europarl German-English task. In contrast to most previous work where phrase models were trained separately from other models used in translation, we include all components such as single word lexica and reordering models in training. Using this consistent training of phrase models we are able to

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Joern Wuebker, Arne Mauser, Hermann Ney
• Keywords: ACL 2010, Approaches Report Problems, Computational Linguistics, Phrase Models, Statistical Machine Translation