Statistical machine translation approaches
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.
The word-based approach is simple, generating one word at a 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 pakistan mobile database 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.
Syntax-Based Statistical Translation
The model translates syntactic units, improving fluency. Because it can interpret some turns of phrases, these translations are more natural-sounding than the phrase-based approach.
Hierarchical phrase-based translation (HPBT)
HPBT is a machine translation approach that uses a phrase-based translation model and a hierarchical language model. Using probabilities, this model captures the syntactic and semantic dependencies between words in a sentence, making it the most commonly used model.