We first train word alignment models and will use them to evaluate the goodness of a phrase and a phrase pair .

Multiple data-driven feature functions are proposed to capture the quality and confidence of phrases and phrase pairs .

The first problem is referred to as phrase pair extraction, which identifies phrase pairs that are supposed to be translations of each other.

The most widely used approach derives phrase pairs from word alignment matrix (Och and Ney, 2003; Koehn et al., 2003).

Other methods do not depend on word alignments only, such as directly modeling phrase alignment in a joint generative way (Marcu and Wong, 2002), pursuing information extraction perspective (Venugopal et al., 2003), or augmenting with model-based phrase pair posterior (Deng and Byrne, 2005).

We combine the strengths of Bayesian modeling and synchronous grammar in unsupervised learning of basic translation phrase pairs .

The structured space of a synchronous grammar is a natural fit for phrase pair probability estimation, though the search space can be prohibitively large.

Computational complexity arises from the exponentially large number of decompositions of a sentence pair into phrase pairs; overfitting is a problem because as EM attempts to maximize the likelihood of its training data, it prefers to directly explain a sentence pair with a single phrase pair .

Our ITG has two nonterminals: X and C, where X represents compositional phrase pairs that can have recursive structures and C is the preterminal over terminal phrase pairs .

They split the left-hand side constituent which represents a phrase pair into two smaller phrase pairs on the right-hand side and order them according to one of the two possible permutations.

where Ze/f P (e / f) = l is a multinomial distribution over phrase pairs .

A sparse prior over a multinomial distribution such as the distribution of phrase pairs may bias the estimator toward skewed distributions that generalize better.

The other is the distribution of the phrase pairs .

By adjusting ac to a very small number, we hope to place more posterior mass on parsimonious solutions with fewer but more confident and general phrase pairs .