  Machine translation has made rapid advances in recent years. Millions of
people are using it today in online translation systems and mobile applications
in order to communicate across language barriers. The question naturally arises
whether such systems can approach or achieve parity with human translations. In
this paper, we first address the problem of how to define and accurately
measure human parity in translation. We then describe Microsoft's machine
translation system and measure the quality of its translations on the widely
used WMT 2017 news translation task from Chinese to English. We find that our
latest neural machine translation system has reached a new state-of-the-art,
and that the translation quality is at human parity when compared to
professional human translations. We also find that it significantly exceeds the
quality of crowd-sourced non-professional translations.
