Less Accurate
First, SMT often relies heavily on statistical methods, which can be less accurate than the neural networks used in NMT. Neural networks process languages similar to the way a human brain decodes words, allowing it to be more adaptable to nuances and context.
Less Natural-Sounding Than NMT
SMT is more difficult to adapt to new languages and domains since it relies on specific rules or patterns that may not generalize well. As anyone who’s learned a language knows, there are exceptions to every rule. So this strict adherence to rules can result in translations that don’t sound natural.
Finally, because SMT systems rely heavily on probabilities, it albania mobile database is often difficult to determine a confidence estimation for SMT machine translation.
Examples of Statistical Machine Translation Software
Google Translate
Google Translate began as a statistical machine translation service in 2006. Now, it is a neural machine translation service, including over 133 languages.
Microsoft Translator
The earlier versions of Microsoft Translator are statistical machine translation examples. Like many other machine translation software companies, it now uses neural machine translation. It is part of Azure Cognitive Services.
SYSTRAN
SYSTRAN was one of the first companies to offer online statistical machine translations. Its commercial machine translation software suite includes a number of tools for translating text.