The paper presents the methodology and the outcome of the compilation and the processing of the Bulgarian X-language Parallel Corpus (Bul-X-Cor) which was integrated as part of the Bulgarian National Corpus (BulNC). We focus on building representative parallel corpora which include a diversity of domains and genres, reflect the relations between Bulgarian and other languages and are consistent in terms of compilation methodology, text representation, metadata description and annotation conventions. The approaches implemented in the construction of Bul-X-Cor include using readily available text collections on the web, manual compilation (by means of Internet browsing) and preferably automatic compilation (by means of web crawling ― general and focused). Certain levels of annotation applied to Bul-X-Cor are taken as obligatory (sentence segmentation and sentence alignment), while others depend on the availability of tools for a particular language (morpho-syntactic tagging, lemmatisation, syntactic parsing, named entity recognition, word sense disambiguation, etc.) or for a particular task (word and clause alignment). To achieve uniformity of the annotation we have either annotated raw data from scratch or transformed the already existing annotation to follow the conventions accepted for BulNC. Finally, actual uses of the corpora are presented and conclusions are drawn with respect to future work.

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.