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