On the other hand, NMT is faster and doesn’t require as much training. For example, Google recently revealed its Zero-Shot Translation, which can translate language without any translated training on language pairs.
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.
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.
Difficult To Determine How Well SMT Will Perform
Finally, because SMT systems rely heavily on probabilities, it is lithuania mobile database 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.