However, the prevailing SMT systems (such as Google Translate or Yahoo! Babel Fish) are phrase-based rather than word-based. Here “phrase” refers to single words or groups of words that tend to occur adjacent to each other. The idea is that phrases are mapped to phrases, for example, the English word pair red wine to French phrases vin rouge, du vin rouge, or le vin rouge. Also, instead of assuming arbitrary word order scrambling, reordering models are used, according to which a given phrase may tend to be swapped with the left or right neighboring phrase or displaced from the neighbors, in the translation process. Furthermore, instead of relying directly on a Bayesian model, as in the word-based approach, phrase-based approaches typically use a log-linear model, allowing for incorporation of features reflecting not only the language model (such as trigram frequencies), the phrase translation model (such as phrase translation frequencies), and the reordering model, but also miscellaneous features such as the number of words created, the number of phrase translations used, and the number of phrase reorderings (with larger penalties for larger displacements).