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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Statistical machine translation (SMT) uses large amounts of language training data to statistically build a knowledge base for translating from one language to another. Before introducing this language data, usually in the form of a parallel set of sentences from both languages, the SMT system has no other linguistic information available to it. With supervised SMT, however, additional linguistic knowledge is allowed in addition to the training data. When translating between languages with little or no common linguistic backgrounds, like English and Japanese, using supervised SMT is extremely useful. By giving the system linguistic rules before training on the parallel corpus, the SMT system can build better alignments between words in both languages.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Statistical machine translation (SMT) uses large amounts of language training data to statistically build a knowledge base for translating from one language to another. Before introducing this language data, usually in the form of a parallel set of sentences from both languages, the SMT system has no other linguistic information available to it. With supervised SMT, however, additional linguistic knowledge is allowed in addition to the training data. When translating between languages with little or no common linguistic backgrounds, like English and Japanese, using supervised SMT is extremely useful. By giving the system linguistic rules before training on the parallel corpus, the SMT system can build better alignments between words in both languages.