Klaus Macherey


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Improved Domain Adaptation for Statistical Machine Translation
Wei Wang | Klaus Macherey | Wolfgang Macherey | Franz Och | Peng Xu
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers

We present a simple and effective infrastructure for domain adaptation for statistical machine translation (MT). To build MT systems for different domains, it trains, tunes and deploys a single translation system that is capable of producing adapted domain translations and preserving the original generic accuracy at the same time. The approach unifies automatic domain detection and domain model parameterization into one system. Experiment results on 20 language pairs demonstrate its viability.


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Model-Based Aligner Combination Using Dual Decomposition
John DeNero | Klaus Macherey
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Language-independent compound splitting with morphological operations
Klaus Macherey | Andrew Dai | David Talbot | Ashok Popat | Franz Och
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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Creating a High-Quality Machine Translation System for a Low-Resource Language: Yiddish
Dmitriy Genzel | Klaus Macherey | Jakob Uszkoreit
Proceedings of Machine Translation Summit XII: Papers


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Large Scale Experiments for Semantic Labeling of Noun Phrases in Raw Text
Louise Guthrie | Roberto Basili | Fabio Zanzotto | Kalina Bontcheva | Hamish Cunningham | David Guthrie | Jia Cui | Marco Cammisa | Jerry Cheng-Chieh Liu | Cassia Farria Martin | Kristiyan Haralambiev | Martin Holub | Klaus Macherey | Fredrick Jelinek
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)


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Comparison of Alignment Templates and Maximum Entropy Models for NLP
Oliver Bender | Klaus Macherey | Franz Josef Och | Hermann Ney
10th Conference of the European Chapter of the Association for Computational Linguistics

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Confidence measures for statistical machine translation
Nicola Ueffing | Klaus Macherey | Hermann Ney
Proceedings of Machine Translation Summit IX: Papers

In this paper, we present several confidence measures for (statistical) machine translation. We introduce word posterior probabilities for words in the target sentence that can be determined either on a word graph or on an N best list. Two alternative confidence measures that can be calculated on N best lists are proposed. The performance of the measures is evaluated on two different translation tasks: on spontaneously spoken dialogues from the domain of appointment scheduling, and on a collection of technical manuals.