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        <title>What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation</title>
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        <namePart type="given">Fabian</namePart>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>Traditional machine translation systems often require heavy feature engineering and the combination of multiple techniques for solving different subproblems. In recent years, several end-to-end learning architectures based on recurrent neural networks have been proposed. Unlike traditional systems, Neural Machine Translation (NMT) systems learn the parameters of the model and require only minimal preprocessing. Memory and time constraints allow to take only a fixed number of words into account, which leads to the out-of-vocabulary (OOV) problem. In this work, we analyze why the OOV problem arises and why it is considered a serious problem in German. We study the effectiveness of compound word splitters for alleviating the OOV problem, resulting in a 2.5+ BLEU points improvement over a baseline on the WMT’14 German-to-English translation task. For English-to-German translation, we use target-side compound splitting through a special syntax during training that allows the model to merge compound words and gain 0.2 BLEU points.</abstract>
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%0 Conference Proceedings
%T What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation
%A Hirschmann, Fabian
%A Nam, Jinseok
%A Fürnkranz, Johannes
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F hirschmann-etal-2016-makes
%X Traditional machine translation systems often require heavy feature engineering and the combination of multiple techniques for solving different subproblems. In recent years, several end-to-end learning architectures based on recurrent neural networks have been proposed. Unlike traditional systems, Neural Machine Translation (NMT) systems learn the parameters of the model and require only minimal preprocessing. Memory and time constraints allow to take only a fixed number of words into account, which leads to the out-of-vocabulary (OOV) problem. In this work, we analyze why the OOV problem arises and why it is considered a serious problem in German. We study the effectiveness of compound word splitters for alleviating the OOV problem, resulting in a 2.5+ BLEU points improvement over a baseline on the WMT’14 German-to-English translation task. For English-to-German translation, we use target-side compound splitting through a special syntax during training that allows the model to merge compound words and gain 0.2 BLEU points.
%U https://aclanthology.org/C16-1301
%P 3199-3208

Markdown (Informal)

[What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation](https://aclanthology.org/C16-1301) (Hirschmann et al., COLING 2016)

• What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation (Hirschmann et al., COLING 2016)

ACL

• Fabian Hirschmann, Jinseok Nam, and Johannes Fürnkranz. 2016. What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3199–3208, Osaka, Japan. The COLING 2016 Organizing Committee.