The quality of machine translation is rapidly evolving. Today one can find
several machine translation systems on the web that provide reasonable
translations, although the systems are not perfect. In some specific domains,
the quality may decrease. A recently proposed approach to this domain is neural
machine translation. It aims at building a jointly-tuned single neural network
that maximizes translation performance, a very different approach from
traditional statistical machine translation. Recently proposed neural machine
translation models often belong to the encoder-decoder family in which a source
sentence is encoded into a fixed length vector that is, in turn, decoded to
generate a translation. The present research examines the effects of different
training methods on a Polish-English Machine Translation system used for
medical data. The European Medicines Agency parallel text corpus was used as
the basis for training of neural and statistical network-based translation
systems. The main machine translation evaluation metrics have also been used in
analysis of the systems. A comparison and implementation of a real-time medical
translator is the main focus of our experiments.

本研究探讨了不同的训练方法对用于医学数据的波兰 - 英语机器翻译系统的影响，使用欧洲药品管理局平行文本语料库作为神经和统计网络翻译系统训练的基础，并对主要机器翻译评估指标进行了分析。该研究的重点是比较和实施实时医学翻译器。