  Pre-editing is the process of modifying the source text (ST) so that it can
be translated by machine translation (MT) in a better quality. Despite the
unpredictability of black-box neural MT (NMT), pre-editing has been deployed in
various practical MT use cases. Although many studies have demonstrated the
effectiveness of pre-editing methods for particular settings, thus far, a deep
understanding of what pre-editing is and how it works for black-box NMT is
lacking. To elicit such understanding, we extensively investigated human
pre-editing practices. We first implemented a protocol to incrementally record
the minimum edits for each ST and collected 6,652 instances of pre-editing
across three translation directions, two MT systems, and four text domains. We
then analysed the instances from three perspectives: the characteristics of the
pre-edited ST, the diversity of pre-editing operations, and the impact of the
pre-editing operations on NMT outputs. Our findings include the following: (1)
enhancing the explicitness of the meaning of an ST and its syntactic structure
is more important for obtaining better translations than making the ST shorter
and simpler, and (2) although the impact of pre-editing on NMT is generally
unpredictable, there are some tendencies of changes in the NMT outputs
depending on the editing operation types.
