  We report on our experiments to train deep neural networks that automatically
translate informalized LaTeX-written Mizar texts into the formal Mizar
language. To the best of our knowledge, this is the first time when neural
networks have been adopted in the formalization of mathematics. Using Luong et
al.'s neural machine translation model (NMT), we tested our aligned
informal-formal corpora against various hyperparameters and evaluated their
results. Our experiments show that our best performing model configurations are
able to generate correct Mizar statements on 65.73\% of the inference data,
with the union of all models covering 79.17\%. These results indicate that
formalization through artificial neural network is a promising approach for
automated formalization of mathematics. We present several case studies to
illustrate our results.
