Search Results for author: Jan-Thorsten Peter

Found 18 papers, 1 papers with code

The RWTH Aachen Machine Translation System for IWSLT 2016

no code implementations IWSLT 2016 Jan-Thorsten Peter, Andreas Guta, Nick Rossenbach, Miguel Graça, Hermann Ney

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.

Machine Translation Translation

There's no Data Like Better Data: Using QE Metrics for MT Data Filtering

no code implementations9 Nov 2023 Jan-Thorsten Peter, David Vilar, Daniel Deutsch, Mara Finkelstein, Juraj Juraska, Markus Freitag

Quality Estimation (QE), the evaluation of machine translation output without the need of explicit references, has seen big improvements in the last years with the use of neural metrics.

Machine Translation NMT +2

Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition

no code implementations EMNLP 2018 Jan-Thorsten Peter, Eugen Beck, Hermann Ney

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.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Local System Voting Feature for Machine Translation System Combination

no code implementations WS 2015 Markus Freitag, Jan-Thorsten Peter, Stephan Peitz, Minwei Feng, Hermann Ney

In this paper, we enhance the traditional confusion network system combination approach with an additional model trained by a neural network.

Machine Translation Sentence +1

Guided Alignment Training for Topic-Aware Neural Machine Translation

1 code implementation AMTA 2016 Wenhu Chen, Evgeny Matusov, Shahram Khadivi, Jan-Thorsten Peter

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.

Domain Adaptation Machine Translation +3

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