The HPBT approach has outperformed conventional phrase-based translation models on a variety of tasks, including machine translation, information retrieval, and question answering. In recent years, the basic concept of HPBT has been extended to other domains such as natural language processing and computer vision.
Pros and Cons
Statistical machine translation has many advantages over traditional rule-based methods of machine translation.
Saves Businesses Money
SMT is much cheaper and faster than rules-based translation south korea mobile database or human translation. As such, it saves you money and valuable time, which is critical when competing in the tech, medical, IT, or e-commerce sectors.
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 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.