Sabine Hunsicker


2016

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Amplexor MTExpert – machine translation adapted to the translation workflow
Alexandru Ceausu | Sabine Hunsicker | Tudy Droumaguet
Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products

2014

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Machine translation quality estimation adapted to the translation workflow
Sabine Hunsicker | Alexandru Ceausu
Proceedings of Translating and the Computer 36

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Pre-ordering of phrase-based machine translation input in translation workflow
Alexandru Ceausu | Sabine Hunsicker
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Word reordering is a difficult task for decoders when the languages involved have a significant difference in syntax. Phrase-based statistical machine translation (PBSMT), preferred in commercial settings due to its maturity, is particularly prone to errors in long range reordering. Source sentence pre-ordering, as a pre-processing step before PBSMT, proved to be an efficient solution that can be achieved using limited resources. We propose a dependency-based pre-ordering model with parameters optimized using a reordering score to pre-order the source sentence. The source sentence is then translated using an existing phrase-based system. The proposed solution is very simple to implement. It uses a hierarchical phrase-based statistical machine translation system (HPBSMT) for pre-ordering, combined with a PBSMT system for the actual translation. We show that the system can provide alternate translations of less post-editing effort in a translation workflow with German as the source language.

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The tara corpus of human-annotated machine translations
Eleftherios Avramidis | Aljoscha Burchardt | Sabine Hunsicker | Maja Popović | Cindy Tscherwinka | David Vilar | Hans Uszkoreit
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Human translators are the key to evaluating machine translation (MT) quality and also to addressing the so far unanswered question when and how to use MT in professional translation workflows. This paper describes the corpus developed as a result of a detailed large scale human evaluation consisting of three tightly connected tasks: ranking, error classification and post-editing.

2013

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A CCG-based Quality Estimation Metric for Statistical Machine Translation Learning from Human Judgments of Machine Translation Output
Maja Popovic | Eleftherios Avramidis | Aljoscha Burchardt | Sabine Hunsicker | Sven Schmeier | Cindy Tscherwinka | David Vilar
Proceedings of Machine Translation Summit XIV: Posters

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Learning from Human Judgments of Machine Translation Output
Maja Popovic | Eleftherios Avramidis | Aljoscha Burchardt | Sabine Hunsicker | Sven Schmeier | Cindy Tscherwinka | David Vilar
Proceedings of Machine Translation Summit XIV: Posters

2012

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Machine Learning for Hybrid Machine Translation
Sabine Hunsicker | Yu Chen | Christian Federmann
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Hybrid Parallel Sentence Mining from Comparable Corpora
Dan Ștefănescu | Radu Ion | Sabine Hunsicker
Proceedings of the 16th Annual conference of the European Association for Machine Translation

2011

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Stochastic Parse Tree Selection for an Existing RBMT System
Christian Federmann | Sabine Hunsicker
Proceedings of the Sixth Workshop on Statistical Machine Translation

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From Statistical Term Extraction to Hybrid Machine Translation
Petra Wolf | Ulrike Bernardi | Christian Federmann | Sabine Hunsicker
Proceedings of the 15th Annual conference of the European Association for Machine Translation

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DFKI’s SC and MT submissions to IWSLT 2011
David Vilar | Eleftherios Avramidis | Maja Popović | Sabine Hunsicker
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

We describe DFKI’s submission to the System Combination and Machine Translation tracks of the 2011 IWSLT Evaluation Campaign. We focus on a sentence selection mechanism which chooses the (hopefully) best sentence among a set of candidates. The rationale behind it is to take advantage of the strengths of each system, especially given an heterogeneous dataset like the one in this evaluation campaign, composed of TED Talks of very different topics. We focus on using features that correlate well with human judgement and, while our primary system still focus on optimizing the BLEU score on the development set, our goal is to move towards optimizing directly the correlation with human judgement. This kind of system is still under development and was used as a secondary submission.

2010

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Further Experiments with Shallow Hybrid MT Systems
Christian Federmann | Andreas Eisele | Yu Chen | Sabine Hunsicker | Jia Xu | Hans Uszkoreit
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

2009

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Combining Multi-Engine Translations with Moses
Yu Chen | Michael Jellinghaus | Andreas Eisele | Yi Zhang | Sabine Hunsicker | Silke Theison | Christian Federmann | Hans Uszkoreit
Proceedings of the Fourth Workshop on Statistical Machine Translation

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Translation Combination using Factored Word Substitution
Christian Federmann | Silke Theison | Andreas Eisele | Hans Uszkoreit | Yu Chen | Michael Jellinghaus | Sabine Hunsicker
Proceedings of the Fourth Workshop on Statistical Machine Translation

2008

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Hybrid machine translation architectures within and beyond the EuroMatrix project
Andreas Eisele | Christian Federmann | Hans Uszkoreit | Hervé Saint-Amand | Martin Kay | Michael Jellinghaus | Sabine Hunsicker | Teresa Herrmann | Yu Chen
Proceedings of the 12th Annual conference of the European Association for Machine Translation