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Parallel texts are a relatively rare language resource, however, theyconstitute a very useful research material with a wide range of applications.This study presents and analyses new methodologies we developed for obtainingsuch data from previously built comparable corpora. The methodologies areautomatic and unsupervised which makes them good for large scale research. Thetask is highly practical as non-parallel multilingual data occur much morefrequently than parallel corpora and accessing them is easy, although parallelsentences are a considerably more useful resource. In this study, we propose amethod of automatic web crawling in order to build topic-aligned comparablecorpora, e.g. based on the Wikipedia or Euronews.com. We also developed newmethods of obtaining parallel sentences from comparable data and proposedmethods of filtration of corpora capable of selecting inconsistent or onlypartially equivalent translations. Our methods are easily scalable to otherlanguages. Evaluation of the quality of the created corpora was performed byanalysing the impact of their use on statistical machine translation systems.Experiments were presented on the basis of the Polish-English language pair fortexts from different domains, i.e. lectures, phrasebooks, film dialogues,European Parliament proceedings and texts contained medicines leaflets. We alsotested a second method of creating parallel corpora based on data fromcomparable corpora which allows for automatically expanding the existing corpusof sentences about a given domain on the basis of analogies found between them.It does not require, therefore, having past parallel resources in order totrain a classifier.

Multi-domain machine translation enhancements by parallel data extraction from comparable corpora