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Although the parallel corpus has an irreplaceable role in machinetranslation, its scale and coverage is still beyond the actual needs.Non-parallel corpus resources on the web have an inestimable potential value inmachine translation and other natural language processing tasks. This articleproposes a semi-supervised transductive learning method for expanding thetraining corpus in statistical machine translation system by extractingparallel sentences from the non-parallel corpus. This method only requires asmall amount of labeled corpus and a large unlabeled corpus to build ahigh-performance classifier, especially for when there is short of labeledcorpus. The experimental results show that by combining the non-parallel corpusalignment and the semi-supervised transductive learning method, we can moreeffectively use their respective strengths to improve the performance ofmachine translation system.

Machine Translation Model based on Non-parallel Corpus and Semi-supervised Transductive Learning