Neural machine translation (NMT)

Neural machine translation is one of multiple approaches to implementing machine translation. In this case, machine translation is enabled by using an artificial neural network. In the beginning, rule-based systems were used, then they were replaced with statistical methods, and nowadays, we benefit from NMT. Compared to previous approaches, NMT has gained more popularity in recent years due to reduced time and memory consumption and better quality output.

NMT models can be trained in various ways based on a corpus of information. When using componentized content, the NMT model can ingest more granularly defined and specific content, due to the how structure content is defined. Being trained on granular data, as opposed to unstructured documents, allows organizations to develop, launch and integrate fully functional NMTs faster. Going one step further, if this structured content is also meta-tagged, the NMT model now has an even higher context level about the content it parses. This means the model will get to an appropriate translated form even faster.

Key benefits

• Increased machine translation precision for commercial or industry-specific content

• Reduced time for finalizing translations