Nick Rossenbach


2022

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Automatic Video Dubbing at AppTek
Mattia Di Gangi | Nick Rossenbach | Alejandro Pérez | Parnia Bahar | Eugen Beck | Patrick Wilken | Evgeny Matusov
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

Video dubbing is the activity of revoicing a video while offering a viewing experience equivalent to the original video. The revoicing usually comes with a changed script, mostly in a different language, and the revoicing should reproduce the original emotions, coherent with the body language, and lip synchronized. In this project, we aim to build an AD system in three phases: (1) voice-over; (2) emotional voice-over; (3) full dubbing, while enhancing the system with human-in-the-loop capabilities for a higher quality.

2019

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Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron
Yunsu Kim | Hendrik Rosendahl | Nick Rossenbach | Jan Rosendahl | Shahram Khadivi | Hermann Ney
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

We propose a novel model architecture and training algorithm to learn bilingual sentence embeddings from a combination of parallel and monolingual data. Our method connects autoencoding and neural machine translation to force the source and target sentence embeddings to share the same space without the help of a pivot language or an additional transformation. We train a multilayer perceptron on top of the sentence embeddings to extract good bilingual sentence pairs from nonparallel or noisy parallel data. Our approach shows promising performance on sentence alignment recovery and the WMT 2018 parallel corpus filtering tasks with only a single model.

2018

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The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task
Nick Rossenbach | Jan Rosendahl | Yunsu Kim | Miguel Graça | Aman Gokrani | Hermann Ney
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the submission of RWTH Aachen University for the De→En parallel corpus filtering task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use several rule-based, heuristic methods to preselect sentence pairs. These sentence pairs are scored with count-based and neural systems as language and translation models. In addition to single sentence-pair scoring, we further implement a simple redundancy removing heuristic. Our best performing corpus filtering system relies on recurrent neural language models and translation models based on the transformer architecture. A model trained on 10M randomly sampled tokens reaches a performance of 9.2% BLEU on newstest2018. Using our filtering and ranking techniques we achieve 34.8% BLEU.

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

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The RWTH Aachen Machine Translation Systems for IWSLT 2017
Parnia Bahar | Jan Rosendahl | Nick Rossenbach | Hermann Ney
Proceedings of the 14th International Conference on Spoken Language Translation

This work describes the Neural Machine Translation (NMT) system of the RWTH Aachen University developed for the English$German tracks of the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2017. We use NMT systems which are augmented by state-of-the-art extensions. Furthermore, we experiment with techniques that include data filtering, a larger vocabulary, two extensions to the attention mechanism and domain adaptation. Using these methods, we can show considerable improvements over the respective baseline systems and our IWSLT 2016 submission.

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.