Machine translation (MT) on its own is generally not good enough to produce highquality translations, so it is common to have humans intervening in the translation process to improve MT output. A typical intervention is post-editing (PE), where a human translator corrects errors in the MT output. Another is interactive translation prediction (ITP), which involves an MT system presenting a translator with translation suggestions they can accept or reject, actions the MT system then uses to present them with new, corrected suggestions. Both Macklovitch (2006) and Koehn (2009) found ITP to be an efficient alternative to unassisted translation in terms of processing time. So far, phrase-based statistical ITP has not yet proven to be faster than PE (Koehn 2009;Sanchis-Trilles et al. 2014;Underwood et al. 2014;Green et al. 2014;Alves et al. 2016;Alabau et al. 2016). In this paper we present the results of an empirical study on translation productivity in ITP with an underlying neural MT system (NITP). Our results show that over half of the professional translators in our study translated faster with NITP compared to PE, and most preferred it over PE. We also examine differences between PE and ITP in other translation productivity indicators and translators' reactions to the technology.