Automatic Learning
Third, statistical methods can be used to automatically learn the translation rules from data, rather than having to be manually specified by experts. This makes it possible to rapidly adapt the translation system to include new languages or domains without needing expensive human expertise.
Generates Multiple Translations
SMT systems can generate multiple translations for a given input, which can be useful for applications such as information retrieval, where different users may have different preferences.
Statistical machine translation can generate more fluent and albania mobile database natural-sounding translations than those produced by traditional rule-based methods.
Disadvantages Of SMT Versus Neural Machine Translation
Requires Large Amounts Of Training Data
SMT can be slower and more resource-intensive than NMT since it requires more complex algorithms and larger training datasets. The complexity of SMT makes it difficult to understand and debug the system.
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.