Shujie Liu, Chi-ho Li, and Ming Zhou
While ITG has many desirable properties
for word alignment, it still suffers from
the limitation of one-to-one matching.
While existing approaches relax this limitation
using phrase pairs, we propose a
ITG formalism, which even handles units
of non-contiguous words, using both
simple and hierarchical phrase pairs. We
also propose a parameter estimation method,
which combines the merits of both
supervised and unsupervised learning,
for the ITG formalism. The ITG alignment
system achieves significant improvement
in both word alignment quality
and translation performance.