CUNI -Bergamot Submission at WMT 22 General Translation Task (2022)

Abstract: We present the CUNI-Bergamot submission for the WMT22 General translation task. We compete in English-Czech direction. Our submission further explores block backtranslation techniques. Compared to the previous work, we measure performance in terms of COMET score and named entities translation accuracy. We evaluate performance of MBR decoding compared to traditional mixed backtranslation training and we show a possible synergy when using both of the techniques simultaneously. The results show that both approaches are effective means of improving translation quality and they yield even better results when combined.

## introduction this work focuses on exploring of two methods used in nmt in order to improve translation quality: backtranslation and minimum bayes risk decoding using neural-based evaluation metric as a utility function. the methods used and related work are presented in the following section. in next section we describe our experimental setting and results. ## methods we describe methods we used to build our system in this section. ## experiments in this section we present our experimental setup and results.