  The effectiveness of a statistical machine translation system (SMT) is very
dependent upon the amount of parallel corpus used in the training phase. For
low-resource language pairs there are not enough parallel corpora to build an
accurate SMT. In this paper, a novel approach is presented to extract bilingual
Persian-Italian parallel sentences from a non-parallel (comparable) corpus. In
this study, English is used as the pivot language to compute the matching
scores between source and target sentences and candidate selection phase.
Additionally, a new monolingual sentence similarity metric, Normalized Google
Distance (NGD) is proposed to improve the matching process. Moreover, some
extensions of the baseline system are applied to improve the quality of
extracted sentences measured with BLEU. Experimental results show that using
the new pivot based extraction can increase the quality of bilingual corpus
significantly and consequently improves the performance of the Persian-Italian
SMT system.
