This paper presents a statistical approach to machine translation, focusing on translating from French to English. The authors argue that statistical methods, which have been successfully applied in automatic speech recognition and natural language processing, are now suitable for machine translation. The core idea is that a translator's task is to find the most probable source sentence S that could have produced a target sentence T. This is achieved by maximizing the probability Pr(S|T), which is calculated using Bayes' theorem. The probability is determined by the product of the language model probability Pr(S) and the translation probability Pr(T|S). The system requires three components: a language model to compute Pr(S), a translation model to compute Pr(T|S), and a search method to find the sentence S that maximizes the product. The language model computes the probability of a sentence based on the probability of individual words and their context. The translation model computes the probability of a target sentence given a source sentence, considering word alignment and fertility (the number of source words that produce a target word). The search method explores possible source sentences to find the one that maximizes the combined probability. The authors describe a simple statistical machine translation system that uses a language model and a translation model to compute probabilities over source-target sentence pairs. They estimate the parameters of these models using a large database of translated text. The system was tested on a set of French sentences, and the results showed that the system could produce accurate translations, with 48% of the sentences being correctly translated or conveying the same meaning as the original. The authors also discuss the limitations of their approach, including the difficulty of handling sentences where the translation of certain words depends on the translation of others. They plan to improve the system by incorporating more data, developing better models for word alignment, and using probabilistic phrase structure grammars to enhance translation accuracy.This paper presents a statistical approach to machine translation, focusing on translating from French to English. The authors argue that statistical methods, which have been successfully applied in automatic speech recognition and natural language processing, are now suitable for machine translation. The core idea is that a translator's task is to find the most probable source sentence S that could have produced a target sentence T. This is achieved by maximizing the probability Pr(S|T), which is calculated using Bayes' theorem. The probability is determined by the product of the language model probability Pr(S) and the translation probability Pr(T|S). The system requires three components: a language model to compute Pr(S), a translation model to compute Pr(T|S), and a search method to find the sentence S that maximizes the product. The language model computes the probability of a sentence based on the probability of individual words and their context. The translation model computes the probability of a target sentence given a source sentence, considering word alignment and fertility (the number of source words that produce a target word). The search method explores possible source sentences to find the one that maximizes the combined probability. The authors describe a simple statistical machine translation system that uses a language model and a translation model to compute probabilities over source-target sentence pairs. They estimate the parameters of these models using a large database of translated text. The system was tested on a set of French sentences, and the results showed that the system could produce accurate translations, with 48% of the sentences being correctly translated or conveying the same meaning as the original. The authors also discuss the limitations of their approach, including the difficulty of handling sentences where the translation of certain words depends on the translation of others. They plan to improve the system by incorporating more data, developing better models for word alignment, and using probabilistic phrase structure grammars to enhance translation accuracy.