We discuss a previously proposed method for augmenting parallel corpora of limited size for the purposes of machine translation through monolingual paraphrasing of the source language. We develop a three-stage shallow paraphrasing procedure to be applied to the Swedish-Bulgarian language pair for which limited parallel resources exist. The source language exhibits specifics not typical of high-density languages already studied in a similar setting. Paraphrases of a highly productive type of compound nouns in Swedish are generated by a corpus-based technique. Certain Swedish noun-phrase types are paraphrased using basic heuristics. Further we introduce noun-phrase morphological variations for better wordform coverage. We evaluate the performance of a phrase-based statistical machine translation system trained on a baseline parallel corpus and on three stages of artificial enlargement of the source-language training data. Paraphrasing is shown to have no effect on performance for the Swedish-English translation task. We show a small, yet consistent, increase in the BLEU score of Swedish-Bulgarian translations of larger token spans on the first enlargement stage. A small improvement in the overall BLEU score of Swedish-Bulgarian translation is achieved on the second enlargement stage. We find that both improvements justify further research into the method for the Swedish-Bulgarian translation task.