Machine translation is the process of automatically translating text from one language to another. It uses advanced algorithms and models to convert words, sentences, and paragraphs from one language into equivalent ones in another language.
Machine translation is done by training large language models on vast amounts of bilingual text data, allowing the model to learn the relationships between words and phrases in different languages. With AI, machine translation systems can handle complex sentence structures, idiomatic expressions, and cultural references, resulting in more natural and accurate translations. When the model encounters new text, it uses this knowledge to generate translations that are as accurate and natural as possible.
There are two main types of AI-powered machine translation systems: rule-based systems and neural machine translation (NMT) systems. Rule-based systems use a set of linguistic rules to translate text, while NMT systems use deep learning models to analyze and generate translations. NMT systems have proven to be more effective in producing accurate and natural translations, making them the preferred choice for most machine translation applications.
A natural language is a language used as a native tongue by a group of speakers, such as English, Spanish, Mandarin, etc.
Semantics in AI refers to the meaning behind words and sentences and how computers understand that meaning.
Large Language Model (LLM)
A Large Language Model (LLM) is an advanced artificial intelligence system that processes and generates human language.