Constraining the phrase-based, joint probability statistical translation model
Alexandra Birch, Chris Callison-Burch, Miles Osborne, Philipp Koehn · 2006
The joint probability model proposed by Marcu and Wong (2002) provides a strong probabilistic framework for phrase-based statistical machine translation (SMT). The model's usefulness is, however, limited by the computational complexity of estimating parameters at the phrase level. We present the first model to use word alignments for constraining the space of phrasal alignments searched during Expectation Maximization (EM) training. Constraining the joint model improves performance, showing results that are very close to state-of-the-art phrase-based models. It also allows it to scale up to larger corpora and therefore be more widely applicable.