To provide the awareness of errors in mt originating from src, the transformer architecture <cite>(Vaswani et al., 2017)</cite> , which is built solely upon attention mechanisms (Bahdanau et al., 2015) , makes it possible to model dependencies without regard to their distance in the input or output sequences and also captures global dependencies between input and output (for our case src, mt, and pe).