Andreas Guta


2020

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Start-Before-End and End-to-End: Neural Speech Translation by AppTek and RWTH Aachen University
Parnia Bahar | Patrick Wilken | Tamer Alkhouli | Andreas Guta | Pavel Golik | Evgeny Matusov | Christian Herold
Proceedings of the 17th International Conference on Spoken Language Translation

AppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020. For the offline task, we create both cascaded and end-to-end speech translation systems, paying attention to careful data selection and weighting. In the cascaded approach, we combine high-quality hybrid automatic speech recognition (ASR) with the Transformer-based neural machine translation (NMT). Our end-to-end direct speech translation systems benefit from pretraining of adapted encoder and decoder components, as well as synthetic data and fine-tuning and thus are able to compete with cascaded systems in terms of MT quality. For simultaneous translation, we utilize a novel architecture that makes dynamic decisions, learned from parallel data, to determine when to continue feeding on input or generate output words. Experiments with speech and text input show that even at low latency this architecture leads to superior translation results.

2017

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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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The RWTH Aachen Machine Translation System for IWSLT 2016
Jan-Thorsten Peter | Andreas Guta | Nick Rossenbach | Miguel Graça | Hermann Ney
Proceedings of the 13th International Conference on Spoken Language Translation

This work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of International Workshop on Spoken Language Translation (IWSLT) 2016. We have participated in the MT track for the German→English language pair employing our state-of-the-art phrase-based system, neural machine translation implementation and our joint translation and reordering decoder. Furthermore, we have applied feed-forward and recurrent neural language and translation models for reranking. The attention-based approach has been used for reranking the n-best lists for both phrasebased and hierarchical setups. On top of these systems, we make use of system combination to enhance the translation quality by combining individually trained systems.

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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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A Comparative Study on Vocabulary Reduction for Phrase Table Smoothing
Yunsu Kim | Andreas Guta | Joern Wuebker | Hermann Ney
Proceedings of the First Conference on Machine Translation: Volume 1, Research 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

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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Extended Translation Models in Phrase-based Decoding
Andreas Guta | Joern Wuebker | Miguel Graça | Yunsu Kim | Hermann Ney
Proceedings of the Tenth Workshop on Statistical Machine Translation

2014

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The RWTH Aachen machine translation systems for IWSLT 2014
Joern Wuebker | Stephan Peitz | Andreas Guta | Hermann Ney
Proceedings of the 11th 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) 2014. We participated in both the MT and SLT tracks for the English→French and German→English language pairs and applied the identical training pipeline and models on both language pairs. Our state-of-the-art phrase-based baseline systems are augmented with maximum expected BLEU training for phrasal, lexical and reordering models. Further, we apply rescoring with novel recurrent neural language and translation models. The same systems are used for the SLT track, where we additionally perform punctuation prediction on the automatic transcriptions employing hierarchical phrase-based translation. We are able to improve RWTH’s 2013 evaluation systems by 1.7-1.8% BLEU absolute.

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Vector Space Models for Phrase-based Machine Translation
Tamer Alkhouli | Andreas Guta | Hermann Ney
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation