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        <title>The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018</title>
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        <namePart type="given">Julian</namePart>
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    <name type="personal">
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        <namePart type="family">Bahar</namePart>
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    <name type="personal">
        <namePart type="given">Yunsu</namePart>
        <namePart type="family">Kim</namePart>
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    <name type="personal">
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    <abstract>This paper describes the statistical machine translation systems developed at RWTH Aachen University for the German→English, English→Turkish and Chinese→English translation tasks of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use ensembles of neural machine translation systems based on the Transformer architecture. Our main focus is on the German→English task where we to all automatic scored first with respect metrics provided by the organizers. We identify data selection, fine-tuning, batch size and model dimension as important hyperparameters. In total we improve by 6.8% BLEU over our last year’s submission and by 4.8% BLEU over the winning system of the 2017 German→English task. In English→Turkish task, we show 3.6% BLEU improvement over the last year’s winning system. We further report results on the Chinese→English task where we improve 2.2% BLEU on average over our baseline systems but stay behind the 2018 winning systems.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6426</identifier>
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        <date>2018-oct</date>
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%0 Conference Proceedings
%T The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018
%A Schamper, Julian
%A Rosendahl, Jan
%A Bahar, Parnia
%A Kim, Yunsu
%A Nix, Arne
%A Ney, Hermann
%S Proceedings of the Third Conference on Machine Translation: Shared Task Papers
%D 2018
%8 oct
%I Association for Computational Linguistics
%C Belgium, Brussels
%F schamper-etal-2018-rwth
%X This paper describes the statistical machine translation systems developed at RWTH Aachen University for the German→English, English→Turkish and Chinese→English translation tasks of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use ensembles of neural machine translation systems based on the Transformer architecture. Our main focus is on the German→English task where we to all automatic scored first with respect metrics provided by the organizers. We identify data selection, fine-tuning, batch size and model dimension as important hyperparameters. In total we improve by 6.8% BLEU over our last year’s submission and by 4.8% BLEU over the winning system of the 2017 German→English task. In English→Turkish task, we show 3.6% BLEU improvement over the last year’s winning system. We further report results on the Chinese→English task where we improve 2.2% BLEU on average over our baseline systems but stay behind the 2018 winning systems.
%R 10.18653/v1/W18-6426
%U https://aclanthology.org/W18-6426
%U https://doi.org/10.18653/v1/W18-6426
%P 496-503

Markdown (Informal)

[The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018](https://aclanthology.org/W18-6426) (Schamper et al., 2018)

• The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018 (Schamper et al., 2018)

ACL

• Julian Schamper, Jan Rosendahl, Parnia Bahar, Yunsu Kim, Arne Nix, and Hermann Ney. 2018. The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 496–503, Belgium, Brussels. Association for Computational Linguistics.