< Machine Translation

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[citation needed]. 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

Hybrid systems

Computer-aided translation

Translation memory

This article is issued from Wikibooks. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.