  Although the problem of similar language translation has been an area of
research interest for many years, yet it is still far from being solved. In
this paper, we study the performance of two popular approaches: statistical and
neural. We conclude that both methods yield similar results; however, the
performance varies depending on the language pair. While the statistical
approach outperforms the neural one by a difference of 6 BLEU points for the
Spanish-Portuguese language pair, the proposed neural model surpasses the
statistical one by a difference of 2 BLEU points for Czech-Polish. In the
former case, the language similarity (based on perplexity) is much higher than
in the latter case. Additionally, we report negative results for the system
combination with back-translation. Our TALP-UPC system submission won 1st place
for Czech-to-Polish and 2nd place for Spanish-to-Portuguese in the official
evaluation of the 1st WMT Similar Language Translation task.
