Machine translation is a process where a computer program is used to translate text from one language to another. This process can be done using a number of different methods, but the most common ones are rule-based methods, statistical methods, and neural network-based methods.
Rule-based methods are the most traditional and simplest method of machine translation. They rely on a set of rules that define how words and phrases should be translated from one language to another. While this method can be accurate, it is often limited by the number of rules that can be defined. As a result, rule-based methods are often not able to handle more complex translations.
Statistical methods are a more advanced form of machine translation. They use large corpora of data that have been previously translated by humans to learn statistical models of how words and phrases are translated from one language to another. While this method can be more accurate than rule-based methods, it is often more expensive and time-consuming to train.
Neural network-based methods are the most advanced form of machine translation. They use artificial neural networks to learn how to translate text from one language to another. Neural networks are a type of machine learning algorithm that are designed to mimic the way that the human brain learns. This method can be very accurate, but it is also very expensive and time-consuming to train.
References:
https://en.wikipedia.org/wiki/Machine_translation
https://en.wikipedia.org/wiki/Rule-based_machine_translation
https://en.wikipedia.org/wiki/Statistical_machine_translation
https://en.wikipedia.org/wiki/Neural_machine_translation