Deep learning has completely changed approaches to machine translation. The initial ways of building machine translation software were based on rules, the next stage was based on statistics and probability theory. But nowadays, with new researches in the deep learning field has created simple solutions based on machine learning that outperform the best expert systems. This paper overviews the main features of machine translation for analyzing open data in legal cases based on recurrent neural networks. The advantages of systems based on RNN using the sequence-to-sequence model against statistical translation systems are also highlighted in the article. Two machine translation systems based on the sequence-to-sequence model were constructed using Keras and PyTorch machine learning libraries. Based on the obtained results, libraries' analysis was done, and their performance comparison. 