 The earliest, and simplest type of translation method was word-based. This was grounded in the following sort of model of how a foreign-language sentence f  (say, in French) is generated from an English sentence e (which we wish to recover, if the target language is English): First, e is generated according to some simple model of English, for instance one based on bigram frequencies. Individual words of e are then assumed to generate individual words of f with some probability, allowing for arbitrary word-order scrambling (or biased in some way). In learning such a model, the possible correspondences and word-translation probabilities can be estimated from parallel English-French corpora, whose sentences and words have been aligned by hand or by statistical techniques. Such a model can then be used for “decoding” a given French sentence f into an English sentence e by Bayesian inference—we derive e as the English sentence with highest posterior probability, given its French “encoding” as f. This is accomplished with dynamic programming algorithms, and might use an intermediate stage where the n best choices of e are computed (for some predetermined n), and subsequently re-ranked discriminatively using features of e and f ignored by the generative model.