Using RBMT Systems to Produce Bilingual Corpus for SMT

This paper proposes a method using the existing Rule-based Machine Translation (RBMT) system as a black box to produce synthetic bilingual corpus, which will be used as training data for the Statistical Machine Translation (SMT) system. We use the existing RBMT system to translate the monolingual corpus into synthetic bilingual corpus. With the synthetic bilingual corpus, we can build an SMT system even if there is no real bilingual corpus. In our experiments using BLEU as a metric, the system achieves a relative improvement of 11.7% over the best RBMT system that is used to produce the synthetic bilingual corpora. We also interpolate the model trained on a real bilingual corpus and the models trained on the synthetic bilingual corpora. The interpolated model achieves an absolute improvement of 0.0245 BLEU score (13.1% relative) as compared with the individual model trained on the real bilingual corpus.

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