The Role of Neural Machine Translation in Professional Translation Practice

Abstract

Neural Machine Translation (NMT), based on transformer architectures, has reshaped professional translation by offering higher fluency and accuracy than traditional methods. In domain-specific areas such as finance and medicine, NMT combined with post-editing significantly boosts productivity and translation quality (Läubli et al., 2019; Zouhar et al., 2021). Translators report that adaptive NMT tools reduce their cognitive load and editing time, especially when systems learn from ongoing corrections (Karimova et al., 2017; Domingo et al., 2019).

Despite its strengths, NMT still performs poorly in literary, idiomatic, and low-resource language contexts (Li et al., 2024). As such, human expertise remains critical for nuanced and culturally sensitive texts. Furthermore, hybrid evaluation approaches—combining BLEU/TER metrics with human feedback—are now essential to assess NMT quality in professional settings (Popović, 2021).

Professional translation is increasingly shaped by ethical considerations: transparency in AI decision-making, fair credit in collaborative workflows, and sustainability concerns about large language models (Feng et al., 2023). As a result, translators today require AI literacy alongside traditional linguistic skills.

NMT is not replacing professional translators but reshaping their role—turning them into post-editors, language technologists, and critical decision-makers in multilingual workflows.