Statistical machine translation has saved businesses a ton of money and time while playing a major role in globalization. In 2022, business owners and their teams must understand the history and capabilities of new technology like machine translation to maintain a competitive edge. Although earlier models weren’t as sophisticated, they formed the foundation for today's neural machine translation systems.
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 afghanistan mobile database 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 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.
What is statistical machine translation?
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.