Why are post-editors needed?

Neural Machine Translation (NMT) models often produce translations that sound less natural due to their difficulty in understanding and generating contextually appropriate text. This issue is evident even in advanced NMT systems like Google, Yandex, and DeepL.

As a result, NMT outputs may contain inaccuracies or fabricated information that a human translator would not typically produce. This limitation is especially critical in applications where maintaining an appropriate tone of voice is essential.

Can We Train an NMT Model?
Yes, we can train an NMT model to achieve context-sensitive, accurate translations. However, this requires feeding the NMT model with bilingual data in aligned formats such as parallel corpora, translation memory (TMX) files, bilingual file formats (SDLXLIFF), or comma-separated/tab-separated values files (CSV or TSV files). For large projects, it is essential to start with human translation to create high-quality training data for the NMT model. 

Do you really need us?
Yes, there are already trained NMT models available, and their performance varies according to the domain and language pairs. We have the expertise to identify the best NMT engine for your projects.