  This paper presents Adam Mickiewicz University's (AMU) submissions to the
constrained track of the WMT 2022 General MT Task. We participated in the
Ukrainian $\leftrightarrow$ Czech translation directions. The systems are a
weighted ensemble of four models based on the Transformer (big) architecture.
The models use source factors to utilize the information about named entities
present in the input. Each of the models in the ensemble was trained using only
the data provided by the shared task organizers. A noisy back-translation
technique was used to augment the training corpora. One of the models in the
ensemble is a document-level model, trained on parallel and synthetic longer
sequences. During the sentence-level decoding process, the ensemble generated
the n-best list. The n-best list was merged with the n-best list generated by a
single document-level model which translated multiple sentences at a time.
Finally, existing quality estimation models and minimum Bayes risk decoding
were used to rerank the n-best list so that the best hypothesis was chosen
according to the COMET evaluation metric. According to the automatic evaluation
results, our systems rank first in both translation directions.
