Discouraged by rules-based translation, researchers at IBM proposed one of the first statistical translation models in 1990, which was only 48% accurate when translating English and French sentences. But the machine reduced the work of human translators by 60%. So, the researchers remained hopeful for the future.
Over the years that followed, researchers made significant progress in other language pairs, including German-English, Chinese-English, and Spanish-English. In 2006, Google Translate introduced its own statistical machine translation model, quickly becoming a household name despite its flaws.
So what is statistical machine translation? How does it pakistan mobile database measure up to other approaches? Let’s take a closer look at SMT, so you can make well-informed decisions when choosing a machine translation model or language service provider to help your business localize.
Statistical machine translation (SMT) is a subfield of [machine translation link to machine translation] that uses mathematical models to translate text from one natural language to another. SMT analyzes prior collections of translations in the language pairing, known as text corpora. This allows the system to determine the probability of an output. Then, it chooses the translation with the highest probability of accuracy.
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.