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The multilingual nature of the world makes translation a crucial requirementtoday. Parallel dictionaries constructed by humans are a widely-availableresource, but they are limited and do not provide enough coverage for goodquality translation purposes, due to out-of-vocabulary words and neologisms.This motivates the use of statistical translation systems, which areunfortunately dependent on the quantity and quality of training data. Suchsystems have a very limited availability especially for some languages and verynarrow text domains. In this research we present our improvements to currentcomparable corpora mining methodologies by re- implementation of the comparisonalgorithms (using Needleman-Wunch algorithm), introduction of a tuning scriptand computation time improvement by GPU acceleration. Experiments are carriedout on bilingual data extracted from the Wikipedia, on various domains. For theWikipedia itself, additional cross-lingual comparison heuristics wereintroduced. The modifications made a positive impact on the quality andquantity of mined data and on the translation quality.

Unsupervised comparable corpora preparation and exploration for bi-lingual translation equivalents