  This new research explores the effects of various training methods on a
Polish to English Statistical Machine Translation system for medical texts.
Various elements of the EMEA parallel text corpora from the OPUS project were
used as the basis for training of phrase tables and language models and for
development, tuning and testing of the translation system. The BLEU, NIST,
METEOR, RIBES and TER metrics have been used to evaluate the effects of various
system and data preparations on translation results. Our experiments included
systems that used POS tagging, factored phrase models, hierarchical models,
syntactic taggers, and many different alignment methods. We also conducted a
deep analysis of Polish data as preparatory work for automatic data correction
such as true casing and punctuation normalization phase.
