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@PHDTHESIS{Freitag:681715,
author = {Freitag, Markus},
othercontributors = {Ney, Hermann and Yvon, Francois},
title = {{I}nvestigations on machine translation system combination},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
reportid = {RWTH-2017-00377},
pages = {1 Online-Ressource (x, 116 Seiten) : Illustrationen,
Diagramme},
year = {2016},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2017; Dissertation, RWTH Aachen University, 2016},
abstract = {Machine translation is a task in the field of natural
language processing whose objective is to translate
documents from one human language into another human
language without any human interaction. There has been
extensive research in the field of machine translation and
many different machine translation approaches have emerged.
Current machine translation systems are based on dif- ferent
paradigms, such as e.g. phrases, phrases with gaps,
hand-written rules, syntactical rules or neural networks.
All approaches have been proven to perform well on several
international evaluation campaigns, but no one has emerged
as the superior approach. In this thesis, we investigate the
combination of different machine translation approaches to
benefit from all of them.The combination of outputs from
multiple machine translation systems has been successfully
applied in state-of-the-art machine translation evaluations
for several years. System combination is a reliable method
to combine the benefits of different machine translation
systems into one single translation output. System
combination relies on the concept of majority voting and the
assumption that different machine translation engines
produce different errors at different positions, but the
majority agrees on a correct translation. Confusion network
decoding has emerged as one of the the most suc- cessful
approaches in combining machine translation outputs. The
main goal of this thesis is to develop novel methods to
improve the translation quality of confusion network system
combination. In this thesis, we introduce a novel system
combination implementation which has been made available as
open-source toolkit to the research community. We extend
previous invented approaches by the addition of several
models and show that our methods produce better or similar
translation results as the previous invented approaches.
Moreover, compared to one single system combination
approach, our implementation is significantly better in
several translation tasks. On top of this high-level
baseline, we extend the confusion network approach with an
additional model learned by a neural network. The system
combination output is typically a combination of the best
available system engines and ignores the output of weaker
translation systems, although they could be helpful in some
situations. We show that our novel model also takes weaker
systems into account and detects the positions where the
weaker systems help to improve the quality of the combined
translation. One of the most important steps in system
combination is the pairwise alignment process between the
different input systems. We introduce a novel alignment
algorithm which is based on the source sentence and improves
the translation quality of our combined translation. In
addition to automatic evaluations, we also let humans
evaluate our novel approach. Furthermore, we investigate the
effect of decoding direction in the commonly used
phrase-based and hierarchical phrase-based machine
translation approaches. We show how to benefit from system
combination and combine different machine translation setups
that are based on different decoding directions. In
addition, we investigate techniques to combine the different
configurations in an earlier stage, e.g. after the alignment
training or the phrase extraction step.Finally, we present
our recent evaluation results that were obtained with our
previously invented methods. We participated in the most
recent international evaluation campaigns and demonstrate
that our methods outperform the translation setups of all
participating top-ranked international research labs in
several language pairs.},
cin = {122010 / 120000},
ddc = {004},
cid = {$I:(DE-82)122010_20140620$ / $I:(DE-82)120000_20140620$},
typ = {PUB:(DE-HGF)11},
urn = {urn:nbn:de:hbz:82-rwth-2017-003774},
doi = {10.18154/RWTH-2017-00377},
url = {https://publications.rwth-aachen.de/record/681715},
}
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