Abstract
Abstract We focus on examining the impact of machine translation (MT) error rate on adequacy and fluency in machine-translated journalistic texts. German is the source language, with significant polysynthetic features in the formation of composites, and the target language is Slovak, with predominantly inflected features. We analyse twelve error categories, which are incorporated into the categorical framework for the analysis of MT errors and correspond to the four-member core MQM-DQF error typology. The results show that the most significant errors are in the categories of lexical semantics, syntactic-semantic correlativeness and grammar.
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