  We describe our NMT systems submitted to the WMT19 shared task in
English-Czech news translation. Our systems are based on the Transformer model
implemented in either Tensor2Tensor (T2T) or Marian framework.
  We aimed at improving the adequacy and coherence of translated documents by
enlarging the context of the source and target. Instead of translating each
sentence independently, we split the document into possibly overlapping
multi-sentence segments. In case of the T2T implementation, this
"document-level"-trained system achieves a $+0.6$ BLEU improvement ($p<0.05$)
relative to the same system applied on isolated sentences. To assess the
potential effect document-level models might have on lexical coherence, we
performed a semi-automatic analysis, which revealed only a few sentences
improved in this aspect. Thus, we cannot draw any conclusions from this weak
evidence.
