|Department of Engineering|
|University of Cambridge > Engineering Department|
Jamie is a Ph.D. student in the Machine Intelligence Laboratory, supervised by Dr Bill Byrne. He is currently researching Statistical Machine Translation, having graduated from the MPhil course in Computer Speech, Text and Internet Technology in 2005.
He is a member of Jesus College.
This paper describes the Cambridge University Engineering Department submission to the Fifth Workshop on Statistical Machine Translation. We report results for the French-English and Spanish-English shared translation tasks in both directions. The CUED system is based on HiFST, a hierarchical phrase-based decoder implemented using weighted finite-state transducers. In the French-English task, we investigate the use of context-dependent alignment models. We also show that lattice minimum Bayes-risk decoding is an effective framework for multi-source translation, leading to large gains in BLEU score.
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 context-dependent models lead to an improvement in alignment quality, and an increase in translation quality when the alignments are used to build a machine translation system.
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.
The Cambridge University Engineering Department phrase-based statistical machine translation system follows a generative model of translation and is implemented by the composition of component models realised as Weighted Finite State Transducers. Our flexible architecture requires no special purpose decoder and readily handles the large-scale natural language processing demands of state-of-the-art machine translation systems. In this paper we describe the CUED participation in the NIST 2008 Arabic-English machine translation evaluation task.