Statistical machine translation : from single-word models to alignment templates
In this work, new approaches for machine translation using statistical methods are described. In addition to the standard source-channel approach to statistical machine translation, a more general approach based on the maximum entropy principle is presented. Various methods for computing single-word alignments using statistical or heuristic models are described. Various smoothing techniques, methods to integrate a conventional dictionary and training methods are analyzed. A detailed evaluation of these models is performed by comparing the automatically produced word alignment with a manually produced reference alignment. Based on these fundamental single-word based alignment models, a new phrase-based translation model - the alignment template model - is suggested. For this model, a training and an efficient search algorithm is developed. For two specific applications (interactive translation and multi-source translation) specific search algorithms are developed. The suggested machine translation approach has been tested for the German-English Verbmobil task, the French-English Hansards task and for Chinese-English news text translation. Often, the obtained results have been significantly better than those obtained with alternative approaches to machine translation.
info:eu-repo/classification/ddc/004, Informatik, statistical machine translation, alignment models, maximum entropy
Networked Digital Library of Theses and Dissertations
Publikationsserver der RWTH Aachen University