Context-dependent alignment models for Statistical Machine Translation

We introduce alignment models for Machine Translation that take into account the context of a source word when determining its translation. Since the use of these contexts alone causes data sparsity problems, we develop a decision tree algorithm for clustering the contexts based on optimisation of the EM auxiliary function. We show that our contextdependent models lead to an improvement in alignment quality, and an increase in translation quality when the alignments are used in Arabic-English and Chinese-English translation.

European language translation with weighted finite state transducers: The CUED MT system for the 2008 ACL workshop on SMT

We describe the Cambridge University Engineering Department phrase-based statistical machine translation system for Spanish-English and French-English translation in the ACL 2008 Third Workshop on Statistical Machine Translation Shared Task. The CUED system follows a generative model of translation and is implemented by composition of component models realised as Weighted Finite State Transducers, without the use of a special-purpose decoder. Details of system tuning for both Europarl and News translation tasks are provided