A Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content

摘要

This work compares the performances achieved by Phrase-Based Statistical Machine Translation systems (PBSMT) and attention-based Neural 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.