Extended Translation Models in Phrase-based Decoding

We propose a novel extended translation model (ETM) to counteract some problems in phrase-based translation: The lack of translation context when using singleword phrases and uncaptured dependencies beyond phrase boundaries. The ETM operates on word-level and augments the IBM models by an additional bilingual word pair and a reordering operation. Its implementation in a phrase-based decoder introduces translation and reordering dependencies for single-word phrases and dependencies across phrase boundaries. More, the model incorporates an explicit treatment of multiple and empty alignments. Its integration outperforms competitive systems that include lexical and phrase translation models as well as hierarchical reordering models on 4 language pairs significantly by +0.7% BLEU on average. Although simpler and using fewer dependencies, the ETM proves to be on par with 7-gram operation sequence models (Durrani et al., 2013b).