Improved Discriminative ITG Alignment using Hierarchical Phrase Pairs and Semi-supervised Training

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.

Publisher  COLING

Details

TypeInproceedings
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