José Carlos Rosales Nuñez
Université Paris Sud, LIMSI, France / Université Paris Saclay, France / INRIA Paris, France
Djamé Seddah
INRIA Paris, France
Guillaume Wisniewski
Université Paris Sud, LIMSI, France / Université Paris Saclay, Franc
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Published in: Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland
Linköping Electronic Conference Proceedings 167:1, p. 2--14
NEALT Proceedings Series 42:1, p. 2--14
Published: 2019-10-02
ISBN: 978-91-7929-995-8
ISSN: 1650-3686 (print), 1650-3740 (online)
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 speci- ficities of UGC that are problematic for sequential NMT architectures and suggests new avenue for improving NMT models.
