Language Modeling for Machine Translation: Effects of Long Term Context Dependency Language Models for Statistical Machine Translation

by Martin Raab

Automatic translation from one language to another is a highly ambitious task, and there is already a long history of people trying to solve this problem. Yet there is no answer to this problem, but Statistical Machine Translation (SMT) emerged as a promising candidate and is until now of primary research interest. Language Models are very important for SMT, and this book is suggesting and evaluating techniques to improve language models. An excellent source of inspiration for this is the field of speech recognition. The reason is that language models have been studied thoroughly for speech recognition, where language models play a similar role. However, few of the numerous approaches for speech recognition language models have been tested on SMT. Three different language model techniques are evaluated in this class base language models, cache language models and sentence mixture language models. Though this book is primarily geared towards SMT, Students and researchers in all areas of language technologies will find a helpful overview of language model techniques in this book.

91 pages, Paperback

First published November 19, 2007