NMT model

Why are post-editors needed?

NMT translations often sound less natural because they sometimes struggle to understand and generate contextually appropriate text. This is a major weakness of advanced NMT models like Google, Yandex, and DeepL.

As a result, their output may include incorrect or fabricated information that a human translator would not typically produce. This limitation can be particularly problematic in critical applications where tone of voice is paramount.

Can we train 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.