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Dissertation: Statistical Machine Translation: From Single-Word Models to Alignment Templates

Author: Och, Franz Josef

Title: Statistical Machine Translation: From Single-Word Models to

Alignment Templates

Year: 2002

University: Technical University Aachen

http://darwin.bth.rwth-aachen.de/opus3/frontdoor.php?source_opus=529&...

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.

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