@inproceedings{rosales-nunez-etal-2019-comparison,
    title = "Comparison between {NMT} and {PBSMT} Performance for Translating Noisy User-Generated Content",
    author = "Rosales N{\'u}{\~n}ez, Jos{\'e} Carlos  and
      Seddah, Djam{\'e}  and
      Wisniewski, Guillaume",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://aclanthology.org/W19-6101",
    pages = "2--14",
    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.",
}