The following technical report is available from
http://aib.informatik.rwth-aachen.de/:
Franz Josef Och
Statistical Machine Translation: From Single-Word Models
to Alignment Templates
2003-06
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.
Regards,
Volker
The following technical report is available from
http://aib.informatik.rwth-aachen.de/:
Jürgen Giesl, René Thiemann,
Peter Schneider-Kamp and Stephan Falke
Improving Dependency Pairs
2003-04
Abstract:
The dependency pair approach is one of the most
powerful techniques for termination and innermost
termination proofs of term rewrite systems (TRSs).
For any TRS, it generates inequality constraints
that have to be satisfied by weakly monotonic
well-founded orders. We improve the dependency
pair approach by considerably reducing the number
of constraints produced for (innermost) termination
proofs.
Moreover, we extend transformation techniques to
manipulate dependency pairs which simplify (innermost)
termination proofs significantly. In order to fully
automate the dependency pair approach, we show how
transformation techniques and the search for suitable
orders can be mechanized efficiently. We implemented
our results in the automated termination prover AProVE
and evaluated them on large collections of examples.
Regards,
Volker