This paper presents the results of the General Machine Translation Task organised as part of the 2023 Conference on Machine Translation (WMT). In the general MT task, participants were asked to build machine translation systems for any of 8 language pairs (corresponding to 14 translation directions), to be evaluated on test sets consisting of up to four different domains. We evaluate system outputs with professional human annotators using a combination of source-based Direct Assessment and scalar quality metric (DA+SQM).

In this paper we show how different types of spelling errors influence the quality of machine translation. We also propose a method to evaluate the impact of spelling errors correction on translation quality without expensive manual work of providing reference translations.