The RWTH Aachen German-English Machine Translation System for WMT 2014
By Jan-Thorsten Peter, Farzad Toutounchi, Joern Wuebker, Hermann Ney. 2014
13 citations recorded by the source corpus.
This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the German!English translation task of the ACL 2014 Eighth Workshop on Statistical Machine Translation (WMT 2014). Both hierarchical and phrase-based SMT systems are applied employing hierarchical phrase reordering and word class language models. For the phrase-based system, we run discriminative phrase training. In addition, we describe our preprocessing pipeline for German!English.
Subfields: Artificial Intelligence, Machine Translation
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In Artificial Intelligence
- Yoshua Bengio
- Percy Liang
- Kyunghyun Cho
- Mohit Bansal
- Quoc V. Le
- Yu Zhang
- Luke S. Zettlemoyer
- Graham Neubig
In Machine Translation
Citations in the corpus
- On Using Very Large Target Vocabulary for Neural Machine Translation (2014, methodology): “…12 million sentences, is identical to the one used in (Kalchbrenner and Blunsom, 2013; Bahdanau et al., 2014; Sutskever et al., 2014 English → German, the corpus was preprocessed, in a manner similar to (Peitz et al., 2014; Li et al., 2014), in order to remove many poorly translated sentences.”
- Simple Recurrent Units for Highly Parallelizable Recurrence (2017, methodology): “Dataset We train translation models on the WMT English → German dataset, a standard benchmark for translation systems (Peitz et al., 2014; Li et al., 2014; Jean et al., 2015).”
- Training RNNs as Fast as CNNs (2017, methodology): “Following standard practice (Peitz et al., 2014; Li et al., 2014; Jean et al., 2015), the training corpus was pre-processed and about 4 million translation pairs are left after processing.”