  Although the parallel corpus has an irreplaceable role in machine
translation, its scale and coverage is still beyond the actual needs.
Non-parallel corpus resources on the web have an inestimable potential value in
machine translation and other natural language processing tasks. This article
proposes a semi-supervised transductive learning method for expanding the
training corpus in statistical machine translation system by extracting
parallel sentences from the non-parallel corpus. This method only requires a
small amount of labeled corpus and a large unlabeled corpus to build a
high-performance classifier, especially for when there is short of labeled
corpus. The experimental results show that by combining the non-parallel corpus
alignment and the semi-supervised transductive learning method, we can more
effectively use their respective strengths to improve the performance of
machine translation system.
