  We introduce an agreement-based approach to learning parallel lexicons and
phrases from non-parallel corpora. The basic idea is to encourage two
asymmetric latent-variable translation models (i.e., source-to-target and
target-to-source) to agree on identifying latent phrase and word alignments.
The agreement is defined at both word and phrase levels. We develop a Viterbi
EM algorithm for jointly training the two unidirectional models efficiently.
Experiments on the Chinese-English dataset show that agreement-based learning
significantly improves both alignment and translation performance.
