We formulate a generative Bayesian model which seeks to explain the observed parallel data through a combination of bilingual and monolingual parameters.

Though the model is trained using parallel data , during testing it has access only to monolingual data.

This setup ensures that we are testing our model’s ability to learn better parameters at training time, rather than its ability to exploit parallel data at test time.

We formulate a generative Bayesian model which seeks to explain the observed parallel data through a combination of bilingual and monolingual parameters.

More recently, there has been a body of work attempting to improve parsing performance by exploiting syntactically annotated parallel data .

In one strand of this work, annotations are assumed only in a resource-rich language and are projected onto a resource-poor language using the parallel data (Hwa et al., 2005; Xi and Hwa, 2005).

In another strand of work, syntactic annotations are assumed on both sides of the parallel data, and a model is trained to exploit the parallel data at test time as well (Smith and Smith, 2004; Burkett and Klein, 2008).