Since 2014, most industries have moved to neural translation systems. However, many studies show that using a hybrid approach, combining SMT and NMT, increases accuracy. Some language service providers agree and use hybrid systems as an added layer of quality protection.
When first introduced in 1990, SMT was seen as a great improvement compared to the traditional rules-based translation. Researchers refined the early models in an attempt to address the challenges. Their efforts gave rise to several different statistical translation approaches.
Word-Based Statistical Machine Translation
The word-based approach is simple, generating one word at a iceland mobile database time. However, it has several disadvantages. It does not account for the syntactic structure of the sentence or the context of the word, which can result in disorganized translations that change the meaning of the original text.
Conventional Phrase-Based Statistical Machine Translation
The model translates sequences of words. This approach is more complex and overcomes the disadvantages of the word-based approach. By interpreting the syntactic structure of the sentence and context, the translation retains the original text’s meaning. However, phrase-based approaches do not sound as natural.