This book presents a variety of statistical machine translation models, such as word-based models (Chapter 4), phrase-based models (Chapter 5), and tree-based models (Chapter 11). When we describe these models, we mostly follow a generative modeling approach. We break up the translation problem (sentence translation) into smaller steps (say, into the translation of phrases) and build component models for these steps using maximum likelihood estimation.
By decomposing the bigger problem into smaller steps we stay within a mathematically coherent formulation of the problem – the decomposition is done using rules such as the chain rule or the Bayes rule. We throw in a few independence assumptions which are less ...
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