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.
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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