  Within the field of Statistical Machine Translation (SMT), the neural
approach (NMT) has recently emerged as the first technology able to challenge
the long-standing dominance of phrase-based approaches (PBMT). In particular,
at the IWSLT 2015 evaluation campaign, NMT outperformed well established
state-of-the-art PBMT systems on English-German, a language pair known to be
particularly hard because of morphology and syntactic differences. To
understand in what respects NMT provides better translation quality than PBMT,
we perform a detailed analysis of neural versus phrase-based SMT outputs,
leveraging high quality post-edits performed by professional translators on the
IWSLT data. For the first time, our analysis provides useful insights on what
linguistic phenomena are best modeled by neural models -- such as the
reordering of verbs -- while pointing out other aspects that remain to be
improved.
