[PDF] Top 20 The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018

... We apply byte-pair encoding (BPE) to segment words into subword units for all language pairs (Sennrich et al., 2016b). Our BPE models are trained jointly for the source and the target lan- guage with the exception of the ... See full document

... German → English and English → German WMT 2017 evaluation campaign. All networks are trained using all given parallel data, back- translated synthetic data, two LSTM layers in the decoder. The rapid corpus has ... See full document

... cal machine translation system of the RWTH Aachen University developed for the translation task of the Fifth Workshop on Statistical Machine ...MT systems are ... See full document

... source translation toolkit Jane ...phrase-based translation (HPBT) (Chiang, 2007) induces a weighted syn- chronous context-free grammar from parallel ...phrase translation probabilities and lexical ... See full document

... phrase-based translation (Chiang, 2007), a weighted synchronous context-free gram- mar is induced from parallel ...chical systems (Vilar et al., 2010; Huck et al., 2012) are: Phrase translation ... See full document

... To check the quality of a filtering approach, we train a transformer model on the top 10M respec- tively top 100M subwords of the scored training data. We mainly focus on the 10M-subsampling results, as this scenario ... See full document

... statistical machine translation systems of Karlsruhe Institute of Technology, RWTH Aachen and LIMSI and the very structural approach of SYS- TRAN produce hypotheses with a huge ... See full document

... The university of edinburgh’s neural MT systems for ...on Machine Translation, Volume 2: Shared Task Papers, pages 389–399, Copenhagen, ... See full document

... UTFPR systems at the WMT 2018 parallel corpus filtering ...Our supervised approach discerns between good and bad translations by training clas- sic binary classification models over an ... See full document

... In addition, we found that there are many long source samples in the test set. As during train- ing we eliminate all samples which are longer than 100 subwords, our system does not perform well in the translation ... See full document

... We investigate whether noisy input sentences and auto-encoding are necessary at later stages of the training. Hence, these features are disabled af- ter the 3rd iteration. The improvements are not sta- tistical ... See full document

... chine translation (SMT) systems devel- oped at RWTH Aachen University for the translation task of the ACL 2013 Eighth Workshop on Statistical Machine Translation ... See full document

... statistical machine translation (SMT) systems developed by RWTH Aachen University for the translation task of the EMNLP 2011 Sixth Workshop on Statistical Machine ... See full document

... Jane systems are: phrase translation probabilities and lexical smoothing probabilities in both trans- lation directions, word and phrase penalty, binary features marking hierarchical phrases, glue rule, and ... See full document

... baseline systems are based on the Transformer model due to the excellent trans- lation performance and fast training thanks to the self-attention ...vocabulary translation, all the words are seg- mented via ... See full document

... our systems with the following set- tings: a maximum sentence length of 80, grow- diag-final-and symmetrization of GIZA++ align- ments, an interpolated Kneser-Ney smoothed 5- gram language model with KenLM ... See full document

... the WMT 2018 Shared News Translation ...networks-based systems: 1) a pure Marian- based neural system and 2) a hybrid system which incorporates OpenNMT- based neural post-editing component ... See full document

... Coling2000 dvi Construction of a Hierarchical Translation Memory S Vogel, H Ney Lehrstuhl f?ur Informatik VI, Computer Science Department RWTH Aachen { University of Technology D 52056 Aachen, Germany[.] ... See full document

... We use the Sockeye implementation of the Transformer (Hieber et al., 2017). For the German → English translation direction we train small Transformer models and for English→German big models as outlined in Vaswani ... See full document

... Hopkins University for the shared translation task of ACL 2016 First Con- ference on Machine Translation (WMT ...neural machine translation model as reranking ... See full document