Jan-Thorsten Peter


2018

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Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition
Jan-Thorsten Peter | Eugen Beck | Hermann Ney
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Training and testing many possible parameters or model architectures of state-of-the-art machine translation or automatic speech recognition system is a cumbersome task. They usually require a long pipeline of commands reaching from pre-processing the training data to post-processing and evaluating the output.

2017

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The QT21 Combined Machine Translation System for English to Latvian
Jan-Thorsten Peter | Hermann Ney | Ondřej Bojar | Ngoc-Quan Pham | Jan Niehues | Alex Waibel | Franck Burlot | François Yvon | Mārcis Pinnis | Valters Šics | Jasmijn Bastings | Miguel Rios | Wilker Aziz | Philip Williams | Frédéric Blain | Lucia Specia
Proceedings of the Second Conference on Machine Translation

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The RWTH Aachen University English-German and German-English Machine Translation System for WMT 2017
Jan-Thorsten Peter | Andreas Guta | Tamer Alkhouli | Parnia Bahar | Jan Rosendahl | Nick Rossenbach | Miguel Graça | Hermann Ney
Proceedings of the Second Conference on Machine Translation

2016

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Alignment-Based Neural Machine Translation
Tamer Alkhouli | Gabriel Bretschner | Jan-Thorsten Peter | Mohammed Hethnawi | Andreas Guta | Hermann Ney
Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

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The QT21/HimL Combined Machine Translation System
Jan-Thorsten Peter | Tamer Alkhouli | Hermann Ney | Matthias Huck | Fabienne Braune | Alexander Fraser | Aleš Tamchyna | Ondřej Bojar | Barry Haddow | Rico Sennrich | Frédéric Blain | Lucia Specia | Jan Niehues | Alex Waibel | Alexandre Allauzen | Lauriane Aufrant | Franck Burlot | Elena Knyazeva | Thomas Lavergne | François Yvon | Mārcis Pinnis | Stella Frank
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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The RWTH Aachen University English-Romanian Machine Translation System for WMT 2016
Jan-Thorsten Peter | Tamer Alkhouli | Andreas Guta | Hermann Ney
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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CharacTer: Translation Edit Rate on Character Level
Weiyue Wang | Jan-Thorsten Peter | Hendrik Rosendahl | Hermann Ney
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Exponentially Decaying Bag-of-Words Input Features for Feed-Forward Neural Network in Statistical Machine Translation
Jan-Thorsten Peter | Weiyue Wang | Hermann Ney
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Guided Alignment Training for Topic-Aware Neural Machine Translation
Wenhu Chen | Evgeny Matusov | Shahram Khadivi | Jan-Thorsten Peter
Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track

In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models. We show that our novel guided alignment training approach improves translation quality on real-life e-commerce texts consisting of product titles and descriptions, overcoming the problems posed by many unknown words and a large type/token ratio. We also show that meta-data associated with input texts such as topic or category information can significantly improve translation quality when used as an additional signal to the decoder part of the network. With both novel features, the BLEU score of the NMT system on a product title set improves from 18.6 to 21.3%. Even larger MT quality gains are obtained through domain adaptation of a general domain NMT system to e-commerce data. The developed NMT system also performs well on the IWSLT speech translation task, where an ensemble of four variant systems outperforms the phrase-based baseline by 2.1% BLEU absolute.

2015

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A Comparison between Count and Neural Network Models Based on Joint Translation and Reordering Sequences
Andreas Guta | Tamer Alkhouli | Jan-Thorsten Peter | Joern Wuebker | Hermann Ney
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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The RWTH Aachen machine translation system for IWSLT 2015
Jan-Thorsten Peter | Farzad Toutounchi | Stephan Peitz | Parnia Bahar | Andreas Guta | Hermann Ney
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

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The RWTH Aachen German-English Machine Translation System for WMT 2015
Jan-Thorsten Peter | Farzad Toutounchi | Joern Wuebker | Hermann Ney
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Local System Voting Feature for Machine Translation System Combination
Markus Freitag | Jan-Thorsten Peter | Stephan Peitz | Minwei Feng | Hermann Ney
Proceedings of the Tenth Workshop on Statistical Machine Translation

2013

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Advancements in Reordering Models for Statistical Machine Translation
Minwei Feng | Jan-Thorsten Peter | Hermann Ney
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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The RWTH Aachen Machine Translation System for WMT 2013
Stephan Peitz | Saab Mansour | Jan-Thorsten Peter | Christoph Schmidt | Joern Wuebker | Matthias Huck | Markus Freitag | Hermann Ney
Proceedings of the Eighth Workshop on Statistical Machine Translation

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The RWTH Aachen machine translation systems for IWSLT 2013
Joern Wuebker | Stephan Peitz | Tamer Alkhouli | Jan-Thorsten Peter | Minwei Feng | Markus Freitag | Hermann Ney
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign International Workshop on Spoken Language Translation (IWSLT) 2013. We participated in the English→French, English↔German, Arabic→English, Chinese→English and Slovenian↔English MT tracks and the English→French and English→German SLT tracks. We apply phrase-based and hierarchical SMT decoders, which are augmented by state-of-the-art extensions. The novel techniques we experimentally evaluate include discriminative phrase training, a continuous space language model, a hierarchical reordering model, a word class language model, domain adaptation via data selection and system combination of standard and reverse order models. By application of these methods we can show considerable improvements over the respective baseline systems.

2012

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Sequence labeling-based reordering model for phrase-based SMT
Minwei Feng | Jan-Thorsten Peter | Hermann Ney
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

For current statistical machine translation system, reordering is still a major problem for language pairs like Chinese-English, where the source and target language have significant word order differences. In this paper, we propose a novel reordering model based on sequence labeling techniques. Our model converts the reordering problem into a sequence labeling problem, i.e. a tagging task. For the given source sentence, we assign each source token a label which contains the reordering information for that token. We also design an unaligned word tag so that the unaligned word phenomenon is automatically implanted in the proposed model. Our reordering model is conditioned on the whole source sentence. Hence it is able to catch the long dependency in the source sentence. Although the learning on large scale task requests notably amounts of computational resources, the decoder makes use of the tagging information as soft constraints. Therefore, the training procedure of our model is computationally expensive for large task while in the test phase (during translation) our model is very efficient. We carried out experiments on five Chinese-English NIST tasks trained with BOLT data. Results show that our model improves the baseline system by 1.32 BLEU 1.53 TER on average.

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Jane 2: Open Source Phrase-based and Hierarchical Statistical Machine Translation
Joern Wuebker | Matthias Huck | Stephan Peitz | Malte Nuhn | Markus Freitag | Jan-Thorsten Peter | Saab Mansour | Hermann Ney
Proceedings of COLING 2012: Demonstration Papers

2011

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Soft string-to-dependency hierarchical machine translation
Jan-Thorsten Peter | Matthias Huck | Hermann Ney | Daniel Stein
Proceedings of the 8th International Workshop on Spoken Language Translation: Papers

In this paper, we dissect the influence of several target-side dependency-based extensions to hierarchical machine translation, including a dependency language model (LM). We pursue a non-restrictive approach that does not prohibit the production of hypotheses with malformed dependency structures. Since many questions remained open from previous and related work, we offer in-depth analysis of the influence of the language model order, the impact of dependency-based restrictions on the search space, and the information to be gained from dependency tree building during decoding. The application of a non-restrictive approach together with an integrated dependency LM scoring is a novel contribution which yields significant improvements for two large-scale translation tasks for the language pairs Chinese–English and German–French.

2007

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Can We Translate Letters?
David Vilar | Jan-Thorsten Peter | Hermann Ney
Proceedings of the Second Workshop on Statistical Machine Translation