Rules-based approaches require rules for each language, and it is difficult to create large dictionaries and compile grammatical rules. So, creating statistical models for multiple languages requires less time and painstaking work than developing separate rule-based systems for each language.
Training With Large Data
Statistical machine translation can use much larger amounts of data than traditional methods, making it possible to train the models on a very large collection of translated texts. This is especially important for low-resource languages, where such data is the only available resource.
Automatic Learning
Third, statistical methods can be used to automatically learn lithuania mobile database 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.
More Fluent, Natural-Sounding Translations
Statistical machine translation can generate more fluent and 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.