Machine Translation/Statistics

Statistical machine translation Language models Language models are used in MT for a) scoring arbitrary sequences of words (tokens) and b) given a sequence of tokens, they predict what token will likely to follow the sequence. Formally, language models are probability distributions over sequences of tokens in a given language. N-gram models Character-based models Recently, it was shown that it is possible to use sub-words, characters or even bytes as basic units for language modelling. There are a few events focused particularly on such models and in general, processing language data on sub-word units, e.g. SCLem 2017. Translation models IBM models 1-5 Phrase-based models Factored translation models Syntax- and tree-based models Synchronous phrase grammar Parallel tree-banks Syntactic rules extraction Decoding Beam search Hybrid systems Computer-aided translation Translation memory