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        <title>Analyzing Neural MT Search and Model Performance</title>
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        <namePart type="given">Jan</namePart>
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            <title>Proceedings of the First Workshop on Neural Machine Translation</title>
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            <namePart type="given">Thang</namePart>
            <namePart type="family">Luong</namePart>
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        <name type="personal">
            <namePart type="given">Alexandra</namePart>
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    <abstract>In this paper, we offer an in-depth analysis about the modeling and search performance. We address the question if a more complex search algorithm is necessary. Furthermore, we investigate the question if more complex models which might only be applicable during rescoring are promising. By separating the search space and the modeling using n-best list reranking, we analyze the influence of both parts of an NMT system independently. By comparing differently performing NMT systems, we show that the better translation is already in the search space of the translation systems with less performance. This results indicate that the current search algorithms are sufficient for the NMT systems. Furthermore, we could show that even a relatively small n-best list of 50 hypotheses already contain notably better translations.</abstract>
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    <identifier type="doi">10.18653/v1/W17-3202</identifier>
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%0 Conference Proceedings
%T Analyzing Neural MT Search and Model Performance
%A Niehues, Jan
%A Cho, Eunah
%A Ha, Thanh-Le
%A Waibel, Alex
%Y Luong, Thang
%Y Birch, Alexandra
%Y Neubig, Graham
%Y Finch, Andrew
%S Proceedings of the First Workshop on Neural Machine Translation
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver
%F niehues-etal-2017-analyzing
%X In this paper, we offer an in-depth analysis about the modeling and search performance. We address the question if a more complex search algorithm is necessary. Furthermore, we investigate the question if more complex models which might only be applicable during rescoring are promising. By separating the search space and the modeling using n-best list reranking, we analyze the influence of both parts of an NMT system independently. By comparing differently performing NMT systems, we show that the better translation is already in the search space of the translation systems with less performance. This results indicate that the current search algorithms are sufficient for the NMT systems. Furthermore, we could show that even a relatively small n-best list of 50 hypotheses already contain notably better translations.
%R 10.18653/v1/W17-3202
%U https://aclanthology.org/W17-3202
%U https://doi.org/10.18653/v1/W17-3202
%P 11-17

Markdown (Informal)

[Analyzing Neural MT Search and Model Performance](https://aclanthology.org/W17-3202) (Niehues et al., NGT 2017)

• Analyzing Neural MT Search and Model Performance (Niehues et al., NGT 2017)

ACL

• Jan Niehues, Eunah Cho, Thanh-Le Ha, and Alex Waibel. 2017. Analyzing Neural MT Search and Model Performance. In Proceedings of the First Workshop on Neural Machine Translation, pages 11–17, Vancouver. Association for Computational Linguistics.