New statistical and syntactic models for machine translation
Syntax-based reordering and specific language modeling
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Significant improvements have been achieved in machine translation (MT) over the past few years, mostly motivated by the appearance of statistical machine translation (SMT) technology, which is currently considered the best way to perform MT of natural languages. The main goal of this book is to enhance the classical SMT models, introducing syntactical knowledge in the pre-translation step by reordering the source side of the corpus. To a great extent, our interest is in the value of syntax in reordering for languages with high word order disparity. A secondary objective consists of determinin...
Significant improvements have been achieved in machine translation (MT) over the past few years, mostly motivated by the appearance of statistical machine translation (SMT) technology, which is currently considered the best way to perform MT of natural languages. The main goal of this book is to enhance the classical SMT models, introducing syntactical knowledge in the pre-translation step by reordering the source side of the corpus. To a great extent, our interest is in the value of syntax in reordering for languages with high word order disparity. A secondary objective consists of determining the potential of different language model (LM) enhancement techniques in order to improve the performance and efficiency of SMT systems. This book should be useful for professionals in the field of Machine Translation, students, researchers, and practitioners interested in modern natural language processing technologies.
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