  In this paper, we attempt to improve Statistical Machine Translation (SMT)
systems on a very diverse set of language pairs (in both directions): Czech -
English, Vietnamese - English, French - English and German - English. To
accomplish this, we performed translation model training, created adaptations
of training settings for each language pair, and obtained comparable corpora
for our SMT systems. Innovative tools and data adaptation techniques were
employed. The TED parallel text corpora for the IWSLT 2015 evaluation campaign
were used to train language models, and to develop, tune, and test the system.
In addition, we prepared Wikipedia-based comparable corpora for use with our
SMT system. This data was specified as permissible for the IWSLT 2015
evaluation. We explored the use of domain adaptation techniques, symmetrized
word alignment models, the unsupervised transliteration models and the KenLM
language modeling tool. To evaluate the effects of different preparations on
translation results, we conducted experiments and used the BLEU, NIST and TER
metrics. Our results indicate that our approach produced a positive impact on
SMT quality.
