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        <title>Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content</title>
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    <name type="personal">
        <namePart type="given">José</namePart>
        <namePart type="given">Carlos</namePart>
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        <namePart type="given">Djamé</namePart>
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    <name type="personal">
        <namePart type="given">Guillaume</namePart>
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            <title>Proceedings of the 22nd Nordic Conference on Computational Linguistics</title>
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            <namePart type="given">Mareike</namePart>
            <namePart type="family">Hartmann</namePart>
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            <namePart type="given">Barbara</namePart>
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    <abstract>This work compares the performances achieved by Phrase-Based Statistical Machine Translation systems (PB-SMT) and attention-based Neuronal Machine Translation systems (NMT) when translating User Generated Content (UGC), as encountered in social medias, from French to English. We show that, contrary to what could be expected, PBSMT outperforms NMT when translating non-canonical inputs. Our error analysis uncovers the specificities of UGC that are problematic for sequential NMT architectures and suggests new avenue for improving NMT models.</abstract>
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%0 Conference Proceedings
%T Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content
%A Rosales Núñez, José Carlos
%A Seddah, Djamé
%A Wisniewski, Guillaume
%Y Hartmann, Mareike
%Y Plank, Barbara
%S Proceedings of the 22nd Nordic Conference on Computational Linguistics
%D 2019
%8 sep–oct
%I Linköping University Electronic Press
%C Turku, Finland
%F rosales-nunez-etal-2019-comparison
%X This work compares the performances achieved by Phrase-Based Statistical Machine Translation systems (PB-SMT) and attention-based Neuronal Machine Translation systems (NMT) when translating User Generated Content (UGC), as encountered in social medias, from French to English. We show that, contrary to what could be expected, PBSMT outperforms NMT when translating non-canonical inputs. Our error analysis uncovers the specificities of UGC that are problematic for sequential NMT architectures and suggests new avenue for improving NMT models.
%U https://aclanthology.org/W19-6101
%P 2-14

Markdown (Informal)

[Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content](https://aclanthology.org/W19-6101) (Rosales Núñez et al., NoDaLiDa 2019)

• Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content (Rosales Núñez et al., NoDaLiDa 2019)

ACL

• José Carlos Rosales Núñez, Djamé Seddah, and Guillaume Wisniewski. 2019. Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, pages 2–14, Turku, Finland. Linköping University Electronic Press.