  Parallel data are an important part of a reliable Statistical Machine
Translation (SMT) system. The more of these data are available, the better the
quality of the SMT system. However, for some language pairs such as
Persian-English, parallel sources of this kind are scarce. In this paper, a
bidirectional method is proposed to extract parallel sentences from English and
Persian document aligned Wikipedia. Two machine translation systems are
employed to translate from Persian to English and the reverse after which an IR
system is used to measure the similarity of the translated sentences. Adding
the extracted sentences to the training data of the existing SMT systems is
shown to improve the quality of the translation. Furthermore, the proposed
method slightly outperforms the one-directional approach. The extracted corpus
consists of about 200,000 sentences which have been sorted by their degree of
similarity calculated by the IR system and is freely available for public
access on the Web.
